added in win-arm64 (#6813)
* added in win-arm64 * fixed MKL arm64 cmake issue * right helix queue * added in win-arm64 * fixed MKL arm64 cmake issue * right helix queue * fixing arm tests * makes x64 test detection better * change test label * fixed onnx files not being included * added new win-arm baselines * baseline changes * fixed build issue * fixed test * one more basleine * fixed pack for mkl redist * .NET update
This commit is contained in:
Michael Sharp
GitHub
Родитель
aeb1ab8f6b
Коммит
db08da61b8
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@ -131,7 +131,30 @@ jobs:
|
|||
|
||||
- template: /build/ci/job-template.yml
|
||||
parameters:
|
||||
name: Windows_x64_Net60
|
||||
architecture: arm64
|
||||
name: Windows_cross_arm64_Net6
|
||||
buildScript: build.cmd
|
||||
customMatrixes:
|
||||
Debug_Build:
|
||||
_configuration: Debug
|
||||
_config_short: DI
|
||||
_includeBenchmarkData: false
|
||||
_targetFramework: net6.0
|
||||
Release_Build:
|
||||
_configuration: Release
|
||||
_config_short: RI
|
||||
_includeBenchmarkData: false
|
||||
_targetFramework: net6.0
|
||||
innerLoop: true
|
||||
vsTestConfiguration: "/Framework:.NETCoreApp,Version=v6.0"
|
||||
pool:
|
||||
name: NetCore-Public
|
||||
demands: ImageOverride -equals 1es-windows-2019-open
|
||||
helixQueue: windows.11.arm64.open
|
||||
|
||||
- template: /build/ci/job-template.yml
|
||||
parameters:
|
||||
name: Windows_x64_Net6
|
||||
buildScript: build.cmd
|
||||
innerLoop: true
|
||||
vsTestConfiguration: "/Framework:.NETCoreApp,Version=v3.1"
|
||||
|
|
@ -164,7 +187,7 @@ jobs:
|
|||
|
||||
- template: /build/ci/job-template.yml
|
||||
parameters:
|
||||
name: Windows_x86_Net60
|
||||
name: Windows_x86_Net6
|
||||
architecture: x86
|
||||
buildScript: build.cmd
|
||||
innerLoop: true
|
||||
|
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|
|||
|
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@ -63,7 +63,7 @@ stages:
|
|||
displayName: Build Native Assets
|
||||
|
||||
- task: PublishBuildArtifacts@1
|
||||
displayName: Publish Linux package assets
|
||||
displayName: Publish Linux_arm package assets
|
||||
inputs:
|
||||
pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets
|
||||
artifactName: pkgassets
|
||||
|
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@ -86,7 +86,7 @@ stages:
|
|||
displayName: Build Native Assets
|
||||
|
||||
- task: PublishBuildArtifacts@1
|
||||
displayName: Publish Linux package assets
|
||||
displayName: Publish Linux_arm64 package assets
|
||||
inputs:
|
||||
pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets
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||||
artifactName: pkgassets
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||||
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@ -133,7 +133,7 @@ stages:
|
|||
displayName: Build Native Assets
|
||||
|
||||
- task: PublishBuildArtifacts@1
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||||
displayName: Publish macOS package assets
|
||||
displayName: Publish macOS_M1 package assets
|
||||
inputs:
|
||||
pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets
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||||
artifactName: pkgassets
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||||
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@ -141,6 +141,30 @@ stages:
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|||
|
||||
- template: /eng/common/templates/steps/generate-sbom.yml
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||||
|
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################################################################################
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- job: Windows_arm64
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||||
################################################################################
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pool:
|
||||
name: NetCore1ESPool-Internal
|
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demands: ImageOverride -equals windows.vs2019.amd64
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steps:
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||||
# Only build native assets to avoid conflicts.
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- script: ./build.cmd -projects $(Build.SourcesDirectory)/src/Native/Native.proj -configuration $(BuildConfig) /p:TargetArchitecture=arm64 /p:CopyPackageAssets=true
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displayName: Build Native Assets
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- task: PublishBuildArtifacts@1
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displayName: Publish Windows_arm64 package assets
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inputs:
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pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets
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artifactName: pkgassets
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artifactType: container
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- template: /eng/common/templates/steps/generate-sbom.yml
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|
||||
# Terminate all dotnet build processes.
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- script: $(Build.SourcesDirectory)/.dotnet/dotnet.exe build-server shutdown
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displayName: Dotnet Server Shutdown
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################################################################################
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- job: Windows_x86
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################################################################################
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@ -78,7 +78,7 @@
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<ApprovalTestsVersion>5.4.7</ApprovalTestsVersion>
|
||||
<BenchmarkDotNetVersion>0.13.1</BenchmarkDotNetVersion>
|
||||
<DotNetRuntime60Version>6.0.9</DotNetRuntime60Version>
|
||||
<DotNetRuntime80Version>8.0.0-preview.3.23174.8</DotNetRuntime80Version>
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||||
<DotNetRuntime80Version>8.0.0</DotNetRuntime80Version>
|
||||
<FluentAssertionVersion>5.10.2</FluentAssertionVersion>
|
||||
<MicrosoftCodeAnalysisTestingVersion>1.1.2-beta1.23431.1</MicrosoftCodeAnalysisTestingVersion>
|
||||
<MicrosoftDotNetXUnitExtensionsVersion>8.0.0-beta.23265.1</MicrosoftDotNetXUnitExtensionsVersion>
|
||||
|
|
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|||
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@ -13,12 +13,20 @@
|
|||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
|
||||
<Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x86\native\*.dll"
|
||||
Condition="'$(PlatformTarget)' == 'x86'">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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||||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
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</Content>
|
||||
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<Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-arm64\native\*.dll"
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Condition="'$(PlatformTarget)' == 'arm64'">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
|
||||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
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</Content>
|
||||
</ItemGroup>
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||||
|
||||
</Project>
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||||
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@ -58,12 +58,21 @@
|
|||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
|
||||
<Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-x86\native\*.dll"
|
||||
Condition="'$(PlatformTarget)' == 'x86'">
|
||||
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
|
||||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
|
||||
<Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-arm64\native\*.dll"
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||||
Condition="'$(PlatformTarget)' == 'arm64'">
|
||||
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
|
||||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
|
||||
</ItemGroup>
|
||||
|
||||
<!-- Work around https://github.com/NuGet/Home/issues/6091 -->
|
||||
|
|
|
|||
|
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@ -86,6 +86,7 @@
|
|||
<!--The path needed to be hardcoded for this to work on our publishing CI-->
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-x86\native" />
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-x64\native" />
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||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-arm64\native"/>
|
||||
</ItemGroup>
|
||||
</Target>
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||||
|
||||
|
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|||
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@ -19,6 +19,13 @@
|
|||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
|
||||
<Content Include="$(MSBuildThisFileDirectory)\..\..\runtimes\win-arm64\nativeassets\netstandard2.0\*.dll"
|
||||
Condition="'$(PlatformTarget)' == 'arm64'">
|
||||
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
|
||||
<Visible>false</Visible>
|
||||
<Link>%(Filename)%(Extension)</Link>
|
||||
</Content>
|
||||
</ItemGroup>
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||||
|
||||
</Project>
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||||
|
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@ -7,6 +7,8 @@
|
|||
<PackageLicenseExpression></PackageLicenseExpression>
|
||||
<PackageDescription>$(MSBuildProjectName) contains the MKL library redistributed as a NuGet package.</PackageDescription>
|
||||
<PackageTags>$(PackageTags) MLNET MKL</PackageTags>
|
||||
<!-- No native dependencies on arm so causes nuget package failure -->
|
||||
<IsPackable Condition="'$(TargetArchitecture)' == 'arm64' Or '$(TargetArchitecture)' == 'arm'">false</IsPackable>
|
||||
<!-- Empty package with no managed dependencies, only native, so no need to warn for empty lib folder/dependencies -->
|
||||
<NoWarn>$(NoWarn);NU5127;NU5128</NoWarn>
|
||||
<TargetsForTfmSpecificBuildOutput>$(TargetsForTfmSpecificBuildOutput);CopyProjectReferencesToPackage</TargetsForTfmSpecificBuildOutput>
|
||||
|
|
|
|||
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@ -26,6 +26,7 @@
|
|||
<!--The path needed to be hardcoded for this to work on our publishing CI-->
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\MatrixFactorizationNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\MatrixFactorizationNative.pdb" TargetPath="..\..\runtimes\win-x86\native"/>
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||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\MatrixFactorizationNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\MatrixFactorizationNative.pdb" TargetPath="..\..\runtimes\win-x64\native"/>
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\MatrixFactorizationNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\MatrixFactorizationNative.pdb" TargetPath="..\..\runtimes\win-arm64\native"/>
|
||||
</ItemGroup>
|
||||
</Target>
|
||||
</Project>
|
||||
|
|
@ -66,6 +66,7 @@
|
|||
<!--The path needed to be hardcoded for this to work on our publishing CI-->
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x86\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-x86\native"/>
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-x64\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-x64\native"/>
|
||||
<BuildOutputInPackage Condition="Exists('$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\LdaNative.pdb')" Include="$(PackageAssetsPath)$(PackageIdFolderName)\runtimes\win-arm64\native\LdaNative.pdb" TargetPath="..\..\runtimes\win-arm64\native"/>
|
||||
</ItemGroup>
|
||||
</Target>
|
||||
</Project>
|
||||
|
|
|
|||
|
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@ -268,6 +268,9 @@ else()
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|||
if (DEFINED ONEDAL_DEVEL_PATH)
|
||||
message("Path to OneDal library defined [${ONEDAL_DEVEL_PATH}] but it is not being built on this arch.")
|
||||
endif()
|
||||
if (DEFINED MKL_LIB_PATH)
|
||||
message("Path to Intel MKL library defined [${MKL_LIB_PATH}] but it is not being built on this arch.")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
add_subdirectory(LdaNative)
|
||||
|
|
|
|||
|
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@ -0,0 +1,56 @@
|
|||
maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
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||||
TRUTH ||======================
|
||||
positive || 132 | 2 | 0.9851
|
||||
negative || 8 | 212 | 0.9636
|
||||
||======================
|
||||
Precision || 0.9429 | 0.9907 |
|
||||
OVERALL 0/1 ACCURACY: 0.971751
|
||||
LOG LOSS/instance: 0.136411
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.857460
|
||||
AUC: 0.994199
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 98 | 7 | 0.9333
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9703 | 0.9693 |
|
||||
OVERALL 0/1 ACCURACY: 0.969605
|
||||
LOG LOSS/instance: 0.118826
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.868476
|
||||
AUC: 0.997577
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995888 (0.0017)
|
||||
Accuracy: 0.970678 (0.0011)
|
||||
Positive precision: 0.956577 (0.0137)
|
||||
Positive recall: 0.959204 (0.0259)
|
||||
Negative precision: 0.979976 (0.0107)
|
||||
Negative recall: 0.975122 (0.0115)
|
||||
Log-loss: 0.127618 (0.0088)
|
||||
Log-loss reduction: 0.862968 (0.0055)
|
||||
F1 Score: 0.957480 (0.0060)
|
||||
AUPRC: 0.992003 (0.0026)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lr /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995888 0.970678 0.956577 0.959204 0.979976 0.975122 0.127618 0.862968 0.95748 0.992003 0.01 100 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=%Output% data=%Data% seed=1 /lr:0.01;/iter:100
|
||||
|
||||
|
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@ -0,0 +1,58 @@
|
|||
maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
PAV calibrator: piecewise function approximation has 5 components.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
PAV calibrator: piecewise function approximation has 6 components.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 133 | 1 | 0.9925
|
||||
negative || 9 | 211 | 0.9591
|
||||
||======================
|
||||
Precision || 0.9366 | 0.9953 |
|
||||
OVERALL 0/1 ACCURACY: 0.971751
|
||||
LOG LOSS/instance: Infinity
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): -Infinity
|
||||
AUC: 0.994403
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 100 | 5 | 0.9524
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9709 | 0.9779 |
|
||||
OVERALL 0/1 ACCURACY: 0.975684
|
||||
LOG LOSS/instance: 0.227705
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.747961
|
||||
AUC: 0.997619
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996011 (0.0016)
|
||||
Accuracy: 0.973718 (0.0020)
|
||||
Positive precision: 0.953747 (0.0171)
|
||||
Positive recall: 0.972459 (0.0201)
|
||||
Negative precision: 0.986580 (0.0087)
|
||||
Negative recall: 0.972849 (0.0138)
|
||||
Log-loss: Infinity (NaN)
|
||||
Log-loss reduction: -Infinity (NaN)
|
||||
F1 Score: 0.962653 (0.0011)
|
||||
AUPRC: 0.992269 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 Infinity -Infinity 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 11.925824 1 -0 1
|
||||
6 0 -0.4527979 0.29372627 0.50170064939900283 0
|
||||
8 0 -3.796255 1E-15 1.4415419267167138E-15 0
|
||||
9 0 -3.8130417 1E-15 1.4415419267167138E-15 0
|
||||
10 0 -4.7285223 1E-15 1.4415419267167138E-15 0
|
||||
11 0 -4.606969 1E-15 1.4415419267167138E-15 0
|
||||
18 1 7.3475924 1 -0 1
|
||||
20 1 6.1389017 1 -0 1
|
||||
21 1 7.1486177 1 -0 1
|
||||
25 1 1.6632223 0.88235295 0.18057223417631088 1
|
||||
28 0 -4.606969 1E-15 1.4415419267167138E-15 0
|
||||
31 0 -4.2645645 1E-15 1.4415419267167138E-15 0
|
||||
32 1 7.198575 1 -0 1
|
||||
35 0 -4.606969 1E-15 1.4415419267167138E-15 0
|
||||
37 0 -1.714282 0.1 0.15200309583369792 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.5451746 0.88235295 0.18057223417631088 1
|
||||
44 1 8.165841 1 -0 1
|
||||
45 0 -4.602255 1E-15 1.4415419267167138E-15 0
|
||||
46 1 5.6216097 1 -0 1
|
||||
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|
||||
485 0 -4.6490927 1E-15 1.4415419267167138E-15 0
|
||||
486 0 -4.649441 1E-15 1.4415419267167138E-15 0
|
||||
488 1 0.95910263 0.6 0.73696553683865695 1
|
||||
490 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
491 1 5.556222 1 -0 1
|
||||
494 0 -0.01569748 0.5942614 1.3013775627342616 0
|
||||
496 0 -5.8810935 1E-15 1.4415419267167138E-15 0
|
||||
498 0 -4.220706 1E-15 1.4415419267167138E-15 0
|
||||
499 0 -4.220706 1E-15 1.4415419267167138E-15 0
|
||||
500 0 -2.9890532 1E-15 1.4415419267167138E-15 0
|
||||
503 0 -3.955172 1E-15 1.4415419267167138E-15 0
|
||||
505 0 -4.4522543 1E-15 1.4415419267167138E-15 0
|
||||
506 1 8.766859 1 -0 1
|
||||
508 0 -4.0031805 1E-15 1.4415419267167138E-15 0
|
||||
509 0 -5.4523587 1E-15 1.4415419267167138E-15 0
|
||||
511 0 -3.7376466 1E-15 1.4415419267167138E-15 0
|
||||
512 0 -4.0031805 1E-15 1.4415419267167138E-15 0
|
||||
515 1 7.3749876 1 -0 1
|
||||
516 0 -5.8810935 1E-15 1.4415419267167138E-15 0
|
||||
518 0 -4.49539 1E-15 1.4415419267167138E-15 0
|
||||
524 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
525 0 -4.746127 1E-15 1.4415419267167138E-15 0
|
||||
526 0 -4.48624 1E-15 1.4415419267167138E-15 0
|
||||
530 1 5.325205 0.93333334 0.09953566740867692 1
|
||||
536 0 -3.4721127 1E-15 1.4415419267167138E-15 0
|
||||
537 0 -3.2213755 1E-15 1.4415419267167138E-15 0
|
||||
542 0 -3.4861355 1E-15 1.4415419267167138E-15 0
|
||||
543 0 -4.220706 1E-15 1.4415419267167138E-15 0
|
||||
545 0 -3.7376466 1E-15 1.4415419267167138E-15 0
|
||||
550 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
551 0 -5.4043503 1E-15 1.4415419267167138E-15 0
|
||||
552 0 -3.2360563 1E-15 1.4415419267167138E-15 0
|
||||
553 0 -1.4225531 1E-15 1.4415419267167138E-15 0
|
||||
554 0 -4.9692993 1E-15 1.4415419267167138E-15 0
|
||||
555 0 -1.7431297 1E-15 1.4415419267167138E-15 0
|
||||
556 0 -2.9508896 1E-15 1.4415419267167138E-15 0
|
||||
562 0 -5.4043503 1E-15 1.4415419267167138E-15 0
|
||||
564 0 -3.7509632 1E-15 1.4415419267167138E-15 0
|
||||
567 0 -3.4350505 1E-15 1.4415419267167138E-15 0
|
||||
568 1 3.5748358 0.9047619 0.14438990028345636 1
|
||||
570 1 6.466877 1 -0 1
|
||||
571 1 9.048693 1 -0 1
|
||||
572 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
573 0 -5.669884 1E-15 1.4415419267167138E-15 0
|
||||
574 1 5.533701 1 -0 1
|
||||
575 0 -3.2213755 1E-15 1.4415419267167138E-15 0
|
||||
576 0 -3.7376466 1E-15 1.4415419267167138E-15 0
|
||||
579 0 -5.4043503 1E-15 1.4415419267167138E-15 0
|
||||
580 0 -3.4869094 1E-15 1.4415419267167138E-15 0
|
||||
583 0 -4.9692993 1E-15 1.4415419267167138E-15 0
|
||||
585 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
587 0 -3.5334377 1E-15 1.4415419267167138E-15 0
|
||||
588 1 4.6442146 0.93333334 0.09953566740867692 1
|
||||
589 0 -4.0031805 1E-15 1.4415419267167138E-15 0
|
||||
591 1 4.243067 0.93333334 0.09953566740867692 1
|
||||
592 1 4.8517904 0.93333334 0.09953566740867692 1
|
||||
595 0 -3.7376466 1E-15 1.4415419267167138E-15 0
|
||||
596 0 -3.9699688 1E-15 1.4415419267167138E-15 0
|
||||
597 0 -2.9706378 1E-15 1.4415419267167138E-15 0
|
||||
598 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
599 0 -2.9381208 1E-15 1.4415419267167138E-15 0
|
||||
601 0 -5.6155596 1E-15 1.4415419267167138E-15 0
|
||||
603 1 3.1762505 0.9047619 0.14438990028345636 1
|
||||
605 1 8.159748 1 -0 1
|
||||
608 1 8.079367 1 -0 1
|
||||
610 1 6.972576 1 -0 1
|
||||
611 1 5.494137 1 -0 1
|
||||
615 0 -3.7192311 1E-15 1.4415419267167138E-15 0
|
||||
616 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
620 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
623 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
625 0 -3.343666 1E-15 1.4415419267167138E-15 0
|
||||
626 1 3.8647957 0.93333334 0.09953566740867692 1
|
||||
628 0 -5.4523587 1E-15 1.4415419267167138E-15 0
|
||||
630 0 -2.7601237 1E-15 1.4415419267167138E-15 0
|
||||
631 0 -3.7376466 1E-15 1.4415419267167138E-15 0
|
||||
632 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
635 0 -4.2170873 1E-15 1.4415419267167138E-15 0
|
||||
636 1 8.162586 1 -0 1
|
||||
637 0 -2.370799 1E-15 1.4415419267167138E-15 0
|
||||
640 0 -4.039895 1E-15 1.4415419267167138E-15 0
|
||||
643 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
646 0 -5.426158 1E-15 1.4415419267167138E-15 0
|
||||
647 0 -5.4890733 1E-15 1.4415419267167138E-15 0
|
||||
648 1 8.579456 1 -0 1
|
||||
650 0 -3.6219683 1E-15 1.4415419267167138E-15 0
|
||||
651 0 -4.9650173 1E-15 1.4415419267167138E-15 0
|
||||
655 0 -4.7037654 1E-15 1.4415419267167138E-15 0
|
||||
658 1 7.546404 1 -0 1
|
||||
659 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
662 0 -5.1684093 1E-15 1.4415419267167138E-15 0
|
||||
663 0 -5.1684093 1E-15 1.4415419267167138E-15 0
|
||||
664 0 -4.2574205 1E-15 1.4415419267167138E-15 0
|
||||
666 0 -3.0702114 1E-15 1.4415419267167138E-15 0
|
||||
667 0 -4.4382315 1E-15 1.4415419267167138E-15 0
|
||||
669 1 6.9817867 1 -0 1
|
||||
671 0 -3.9565368 1E-15 1.4415419267167138E-15 0
|
||||
672 0 -4.921291 1E-15 1.4415419267167138E-15 0
|
||||
673 0 -3.289723 1E-15 1.4415419267167138E-15 0
|
||||
674 0 -5.669884 1E-15 1.4415419267167138E-15 0
|
||||
675 0 -3.7340279 1E-15 1.4415419267167138E-15 0
|
||||
676 0 -5.419147 1E-15 1.4415419267167138E-15 0
|
||||
677 0 -4.0031805 1E-15 1.4415419267167138E-15 0
|
||||
684 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
686 0 -5.935418 1E-15 1.4415419267167138E-15 0
|
||||
687 0 -4.295347 1E-15 1.4415419267167138E-15 0
|
||||
690 0 -5.4890733 1E-15 1.4415419267167138E-15 0
|
||||
695 0 -5.4523587 1E-15 1.4415419267167138E-15 0
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 133 | 1 | 0.9925
|
||||
negative || 9 | 211 | 0.9591
|
||||
||======================
|
||||
Precision || 0.9366 | 0.9953 |
|
||||
OVERALL 0/1 ACCURACY: 0.971751
|
||||
LOG LOSS/instance: 0.139629
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.854097
|
||||
AUC: 0.994403
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 100 | 5 | 0.9524
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9709 | 0.9779 |
|
||||
OVERALL 0/1 ACCURACY: 0.975684
|
||||
LOG LOSS/instance: 0.121001
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.866069
|
||||
AUC: 0.997619
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996011 (0.0016)
|
||||
Accuracy: 0.973718 (0.0020)
|
||||
Positive precision: 0.953747 (0.0171)
|
||||
Positive recall: 0.972459 (0.0201)
|
||||
Negative precision: 0.986580 (0.0087)
|
||||
Negative recall: 0.972849 (0.0138)
|
||||
Log-loss: 0.130315 (0.0093)
|
||||
Log-loss reduction: 0.860083 (0.0060)
|
||||
F1 Score: 0.962653 (0.0011)
|
||||
AUPRC: 0.992269 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 0.130315 0.860083 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 11.925824 0.99988866 0.00016064073906035849 1
|
||||
6 0 -0.4527979 0.28799012 0.49003084056125074 0
|
||||
8 0 -3.796255 0.026382547 0.038573064761285226 0
|
||||
9 0 -3.8130417 0.026036168 0.038059895132779242 0
|
||||
10 0 -4.7285223 0.0125917215 0.018281354087045291 0
|
||||
11 0 -4.606969 0.013873976 0.02015606392328537 0
|
||||
18 1 7.3475924 0.99550986 0.006492485535140746 1
|
||||
20 1 6.1389017 0.9881576 0.017186984398807038 1
|
||||
21 1 7.1486177 0.9947303 0.0076226814052998944 1
|
||||
25 1 1.6632223 0.6911742 0.53287870995530984 1
|
||||
28 0 -4.606969 0.013873976 0.02015606392328537 0
|
||||
31 0 -4.2645645 0.01821836 0.026525908568759592 0
|
||||
32 1 7.198575 0.99493784 0.0073217047835288053 1
|
||||
35 0 -4.606969 0.013873976 0.02015606392328537 0
|
||||
37 0 -1.714282 0.12729812 0.19643918434677948 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.5451746 0.8203416 0.28570332741079429 1
|
||||
44 1 8.165841 0.9976778 0.0033541180091675089 1
|
||||
45 0 -4.602255 0.0139262155 0.020232492307431734 0
|
||||
46 1 5.6216097 0.9821182 0.026031444169057363 1
|
||||
48 0 -3.379683 0.03656119 0.053735055635659848 0
|
||||
50 1 2.8003244 0.8487653 0.2365623958486662 1
|
||||
51 1 0.14775276 0.39660606 1.3342213809867165 1
|
||||
52 1 4.696246 0.9629518 0.054464539198696107 1
|
||||
54 1 6.743868 0.9927051 0.01056288089528439 1
|
||||
56 1 6.5947094 0.9917779 0.011911021598241851 1
|
||||
60 1 2.2064123 0.7763946 0.36513800059720591 1
|
||||
63 1 0.8789625 0.5427823 0.88155440156950038 1
|
||||
64 0 -4.8905344 0.011062949 0.016049403073119514 0
|
||||
66 0 -3.697434 0.028514326 0.041735373036084926 0
|
||||
68 1 9.899808 0.99942744 0.00082626923994757486 1
|
||||
69 0 -4.3595524 0.016894378 0.024581670707795066 0
|
||||
70 0 -3.0557137 0.046994247 0.069443170930934078 0
|
||||
71 1 7.555621 0.9962023 0.0054893674226172791 1
|
||||
72 0 -1.6769085 0.13069288 0.20206214111088219 0
|
||||
73 1 7.7111273 0.9966495 0.0048418602396339801 1
|
||||
74 1 2.4994192 0.8148248 0.29543816942188406 1
|
||||
76 0 -3.8396955 0.02549526 0.037258890759872848 0
|
||||
77 0 -3.1438046 0.043905534 0.064774925308085776 0
|
||||
79 0 -4.4265766 0.016017534 0.023295486850883066 0
|
||||
82 0 -3.1870723 0.042460293 0.062595780681733368 0
|
||||
88 0 -3.697434 0.028514326 0.041735373036084926 0
|
||||
90 0 -4.54813 0.01454009 0.021130913771998817 0
|
||||
91 0 -4.5069323 0.015025148 0.021841204430750202 0
|
||||
92 0 -3.697434 0.028514326 0.041735373036084926 0
|
||||
93 0 -4.8905344 0.011062949 0.016049403073119514 0
|
||||
95 0 -4.54813 0.01454009 0.021130913771998817 0
|
||||
96 0 -4.790498 0.011983715 0.017393273625651583 0
|
||||
97 0 -3.413869 0.0356 0.052296442645105176 0
|
||||
98 1 9.294541 0.99906635 0.0013475973096922743 1
|
||||
99 1 9.621308 0.99928296 0.001034846704541113 1
|
||||
100 1 5.1074314 0.97314864 0.039267908030735391 1
|
||||
102 0 -3.3471546 0.037498932 0.055139953861279192 0
|
||||
104 1 11.120679 0.99978656 0.00030796780451509731 1
|
||||
105 1 2.1430416 0.7673742 0.38199780127191252 1
|
||||
106 1 8.747506 0.99854773 0.0020967014982888315 1
|
||||
108 0 -4.5133796 0.0149481995 0.021728501937672035 0
|
||||
109 1 6.3912544 0.99032205 0.014030327944048709 1
|
||||
111 1 4.1208715 0.9422745 0.085780676489496735 1
|
||||
112 1 7.006652 0.9940932 0.0085470080951718218 1
|
||||
113 1 9.855811 0.9994067 0.00085621167564539895 1
|
||||
115 0 -3.4127908 0.03562994 0.05234123234660417 0
|
||||
117 1 8.638749 0.99841446 0.0022892705964363013 1
|
||||
120 0 -4.0389752 0.021784049 0.031775104426885928 0
|
||||
121 0 -3.0156007 0.048468217 0.071676249166093706 0
|
||||
122 1 10.533509 0.9996569 0.00049505099616089013 1
|
||||
123 1 4.6804914 0.9624947 0.055149541356901512 1
|
||||
125 0 -4.8905344 0.011062949 0.016049403073119514 0
|
||||
128 1 4.9510326 0.9696393 0.044479919754725615 1
|
||||
129 0 -3.789802 0.02651689 0.038772146811620695 0
|
||||
131 0 -4.2645645 0.01821836 0.026525908568759592 0
|
||||
132 1 9.206267 0.99899733 0.0014472717652274012 1
|
||||
133 0 -4.1348257 0.020192467 0.029429710954844902 0
|
||||
137 0 -4.6658077 0.013237966 0.019225886685070071 0
|
||||
138 0 -3.6895585 0.028691236 0.041998115839947503 0
|
||||
141 0 -4.9493732 0.010554424 0.015307739563326032 0
|
||||
144 0 -4.606969 0.013873976 0.02015606392328537 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -4.2551055 0.018355653 0.026727668422101055 0
|
||||
150 0 -4.7285223 0.0125917215 0.018281354087045291 0
|
||||
151 1 5.0748987 0.97245276 0.040299926989301821 1
|
||||
152 1 8.881612 0.99869674 0.0018814265868558253 1
|
||||
154 0 -5.232939 0.0084102405 0.012184722644637337 0
|
||||
156 0 -4.3134584 0.017524607 0.025506821392282504 0
|
||||
161 0 -3.5088568 0.03305537 0.048494813867653343 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 7.490014 0.99599624 0.0057878035564347926 1
|
||||
169 0 -5.254455 0.008266402 0.011975463236807289 0
|
||||
171 0 -4.54813 0.01454009 0.021130913771998817 0
|
||||
173 1 14.494651 0.99998605 2.0122110477710732E-05 1
|
||||
174 1 5.7514915 0.983872 0.023457464452347754 1
|
||||
176 0 -4.2645645 0.01821836 0.026525908568759592 0
|
||||
177 1 6.138485 0.98815364 0.017192727854047185 1
|
||||
179 1 2.8749352 0.8563466 0.22373324768939593 1
|
||||
180 0 -4.7285223 0.0125917215 0.018281354087045291 0
|
||||
181 0 -5.232939 0.0084102405 0.012184722644637337 0
|
||||
183 1 9.06396 0.9988752 0.0016236556168074772 1
|
||||
187 1 12.00492 0.9998956 0.00015066466904787792 1
|
||||
188 1 8.1324835 0.99761444 0.0034457424608055095 1
|
||||
189 0 -3.6207743 0.030282307 0.044363288606634702 0
|
||||
191 1 10.797473 0.99972284 0.00039991508636870194 1
|
||||
192 0 -3.7562728 0.027225707 0.039822990547268275 0
|
||||
196 0 5.8725023 0.9853531 6.0932620413355663 1
|
||||
198 0 -5.232939 0.0084102405 0.012184722644637337 0
|
||||
199 0 -4.3234034 0.017386708 0.025304340651562268 0
|
||||
201 1 9.770606 0.99936444 0.00091721700916576874 1
|
||||
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536 0 -3.4721127 0.041149363 0.060621994898335516 0
|
||||
537 0 -3.2213755 0.049611423 0.073410599070621044 0
|
||||
542 0 -3.4861355 0.040719297 0.059975058703050588 0
|
||||
543 0 -4.220706 0.023354556 0.034093186342205803 0
|
||||
545 0 -3.7376466 0.033700433 0.049457579363906538 0
|
||||
550 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
551 0 -5.4043503 0.009396285 0.013620063190565418 0
|
||||
552 0 -3.2360563 0.049073454 0.07259418997779464 0
|
||||
553 0 -1.4225531 0.17546193 0.27834199385026431 0
|
||||
554 0 -4.9692993 0.013149481 0.019096523087584735 0
|
||||
555 0 -1.7431297 0.14211455 0.22114307136971645 0
|
||||
556 0 -2.9508896 0.060577318 0.090153667664306186 0
|
||||
562 0 -5.4043503 0.009396285 0.013620063190565418 0
|
||||
564 0 -3.7509632 0.033363298 0.04895432111111532 0
|
||||
567 0 -3.4350505 0.042307038 0.062364895082251487 0
|
||||
568 1 3.5748358 0.913456 0.13059282121029733 1
|
||||
570 1 6.466877 0.99020326 0.014203393857934222 1
|
||||
571 1 9.048693 0.99868524 0.0018980446658527542 1
|
||||
572 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
573 0 -5.669884 0.0076494967 0.011078316903702529 0
|
||||
574 1 5.533701 0.9799025 0.029289877586105746 1
|
||||
575 0 -3.2213755 0.049611423 0.073410599070621044 0
|
||||
576 0 -3.7376466 0.033700433 0.049457579363906538 0
|
||||
579 0 -5.4043503 0.009396285 0.013620063190565418 0
|
||||
580 0 -3.4869094 0.04069569 0.059939555518058511 0
|
||||
583 0 -4.9692993 0.013149481 0.019096523087584735 0
|
||||
585 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
587 0 -3.5334377 0.039300125 0.057842295114820461 0
|
||||
588 1 4.6442146 0.9605316 0.058095029247836778 1
|
||||
589 0 -4.0031805 0.02756116 0.040320577663836212 0
|
||||
591 1 4.243067 0.9467788 0.078900732087929318 1
|
||||
592 1 4.8517904 0.96624035 0.049545999536437443 1
|
||||
595 0 -3.7376466 0.033700433 0.049457579363906538 0
|
||||
596 0 -3.9699688 0.028265119 0.041365338113873051 0
|
||||
597 0 -2.9706378 0.05970531 0.08881512342261072 0
|
||||
598 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
599 0 -2.9381208 0.061147466 0.09102952445231563 0
|
||||
601 0 -5.6155596 0.007978472 0.011556665699955653 0
|
||||
603 1 3.1762505 0.88546234 0.17549714050514675 1
|
||||
605 1 8.159748 0.99737054 0.0037985035173251067 1
|
||||
608 1 8.079367 0.9972006 0.0040443320699722306 1
|
||||
610 1 6.972576 0.9933793 0.0095834180703139316 1
|
||||
611 1 5.494137 0.9792847 0.030199744813973507 1
|
||||
615 0 -3.7192311 0.03417207 0.05016190956415939 0
|
||||
616 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
620 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
623 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
625 0 -3.343666 0.045295946 0.066874509831910151 0
|
||||
626 1 3.8647957 0.929766 0.10506042608942295 1
|
||||
628 0 -5.4523587 0.00905354 0.013120983569838577 0
|
||||
630 0 -2.7601237 0.069634475 0.10413045633568027 0
|
||||
631 0 -3.7376466 0.033700433 0.049457579363906538 0
|
||||
632 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
635 0 -4.2170873 0.023419123 0.034188567100013993 0
|
||||
636 1 8.162586 0.9973763 0.0037901403924413767 1
|
||||
637 0 -2.370799 0.092107005 0.13940582521487643 0
|
||||
640 0 -4.039895 0.026802778 0.039195893152521553 0
|
||||
643 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
646 0 -5.426158 0.00923903 0.013391058760089801 0
|
||||
647 0 -5.4890733 0.008799812 0.012751633519735472 0
|
||||
648 1 8.579456 0.9981042 0.0027376359023654488 1
|
||||
650 0 -3.6219683 0.036770534 0.054048568515248828 0
|
||||
651 0 -4.9650173 0.01319296 0.019160086942209224 0
|
||||
655 0 -4.7037654 0.016131794 0.023463022615813851 0
|
||||
658 1 7.546404 0.99576104 0.006128529583862446 1
|
||||
659 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
662 0 -5.1684093 0.011276632 0.016361164283329451 0
|
||||
663 0 -5.1684093 0.011276632 0.016361164283329451 0
|
||||
664 0 -4.2574205 0.02270922 0.033140214869637266 0
|
||||
666 0 -3.0702114 0.055484954 0.08235431655806906 0
|
||||
667 0 -4.4382315 0.01977694 0.028818008476875413 0
|
||||
669 1 6.9817867 0.99342644 0.0095149472206866505 1
|
||||
671 0 -3.9565368 0.028554758 0.041795418071838834 0
|
||||
672 0 -4.921291 0.013645153 0.019821336171893227 0
|
||||
673 0 -3.289723 0.047153607 0.069684436821807083 0
|
||||
674 0 -5.669884 0.0076494967 0.011078316903702529 0
|
||||
675 0 -3.7340279 0.03379261 0.049595209486744243 0
|
||||
676 0 -5.419147 0.009289299 0.013464259841054335 0
|
||||
677 0 -4.0031805 0.02756116 0.040320577663836212 0
|
||||
684 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
686 0 -5.935418 0.006225399 0.0090094253868573462 0
|
||||
687 0 -4.295347 0.022060877 0.032183434683621268 0
|
||||
690 0 -5.4890733 0.008799812 0.012751633519735472 0
|
||||
695 0 -5.4523587 0.00905354 0.013120983569838577 0
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Not training a calibrator because a valid calibrator trainer was not provided.
|
||||
Warning: Data does not contain a probability column. Will not output the Log-loss column
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)
|
||||
Not training a calibrator because a valid calibrator trainer was not provided.
|
||||
Warning: Data does not contain a probability column. Will not output the Log-loss column
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 133 | 1 | 0.9925
|
||||
negative || 9 | 211 | 0.9591
|
||||
||======================
|
||||
Precision || 0.9366 | 0.9953 |
|
||||
OVERALL 0/1 ACCURACY: 0.971751
|
||||
LOG LOSS/instance: NaN
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.000000
|
||||
AUC: 0.994403
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 100 | 5 | 0.9524
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9709 | 0.9779 |
|
||||
OVERALL 0/1 ACCURACY: 0.975684
|
||||
LOG LOSS/instance: NaN
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.000000
|
||||
AUC: 0.997619
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996011 (0.0016)
|
||||
Accuracy: 0.973718 (0.0020)
|
||||
Positive precision: 0.953747 (0.0171)
|
||||
Positive recall: 0.972459 (0.0201)
|
||||
Negative precision: 0.986580 (0.0087)
|
||||
Negative recall: 0.972849 (0.0138)
|
||||
Log-loss: NaN (NaN)
|
||||
Log-loss reduction: 0.000000 (0.0000)
|
||||
F1 Score: 0.962653 (0.0011)
|
||||
AUPRC: 0.992269 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 NaN 0 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Assigned
|
||||
5 1 11.925824 1
|
||||
6 0 -0.4527979 0
|
||||
8 0 -3.796255 0
|
||||
9 0 -3.8130417 0
|
||||
10 0 -4.7285223 0
|
||||
11 0 -4.606969 0
|
||||
18 1 7.3475924 1
|
||||
20 1 6.1389017 1
|
||||
21 1 7.1486177 1
|
||||
25 1 1.6632223 1
|
||||
28 0 -4.606969 0
|
||||
31 0 -4.2645645 0
|
||||
32 1 7.198575 1
|
||||
35 0 -4.606969 0
|
||||
37 0 -1.714282 0
|
||||
40 0 ? 0
|
||||
41 1 2.5451746 1
|
||||
44 1 8.165841 1
|
||||
45 0 -4.602255 0
|
||||
46 1 5.6216097 1
|
||||
48 0 -3.379683 0
|
||||
50 1 2.8003244 1
|
||||
51 1 0.14775276 1
|
||||
52 1 4.696246 1
|
||||
54 1 6.743868 1
|
||||
56 1 6.5947094 1
|
||||
60 1 2.2064123 1
|
||||
63 1 0.8789625 1
|
||||
64 0 -4.8905344 0
|
||||
66 0 -3.697434 0
|
||||
68 1 9.899808 1
|
||||
69 0 -4.3595524 0
|
||||
70 0 -3.0557137 0
|
||||
71 1 7.555621 1
|
||||
72 0 -1.6769085 0
|
||||
73 1 7.7111273 1
|
||||
74 1 2.4994192 1
|
||||
76 0 -3.8396955 0
|
||||
77 0 -3.1438046 0
|
||||
79 0 -4.4265766 0
|
||||
82 0 -3.1870723 0
|
||||
88 0 -3.697434 0
|
||||
90 0 -4.54813 0
|
||||
91 0 -4.5069323 0
|
||||
92 0 -3.697434 0
|
||||
93 0 -4.8905344 0
|
||||
95 0 -4.54813 0
|
||||
96 0 -4.790498 0
|
||||
97 0 -3.413869 0
|
||||
98 1 9.294541 1
|
||||
99 1 9.621308 1
|
||||
100 1 5.1074314 1
|
||||
102 0 -3.3471546 0
|
||||
104 1 11.120679 1
|
||||
105 1 2.1430416 1
|
||||
106 1 8.747506 1
|
||||
108 0 -4.5133796 0
|
||||
109 1 6.3912544 1
|
||||
111 1 4.1208715 1
|
||||
112 1 7.006652 1
|
||||
113 1 9.855811 1
|
||||
115 0 -3.4127908 0
|
||||
117 1 8.638749 1
|
||||
120 0 -4.0389752 0
|
||||
121 0 -3.0156007 0
|
||||
122 1 10.533509 1
|
||||
123 1 4.6804914 1
|
||||
125 0 -4.8905344 0
|
||||
128 1 4.9510326 1
|
||||
129 0 -3.789802 0
|
||||
131 0 -4.2645645 0
|
||||
132 1 9.206267 1
|
||||
133 0 -4.1348257 0
|
||||
137 0 -4.6658077 0
|
||||
138 0 -3.6895585 0
|
||||
141 0 -4.9493732 0
|
||||
144 0 -4.606969 0
|
||||
145 0 ? 0
|
||||
147 0 -4.2551055 0
|
||||
150 0 -4.7285223 0
|
||||
151 1 5.0748987 1
|
||||
152 1 8.881612 1
|
||||
154 0 -5.232939 0
|
||||
156 0 -4.3134584 0
|
||||
161 0 -3.5088568 0
|
||||
164 0 ? 0
|
||||
167 1 7.490014 1
|
||||
169 0 -5.254455 0
|
||||
171 0 -4.54813 0
|
||||
173 1 14.494651 1
|
||||
174 1 5.7514915 1
|
||||
176 0 -4.2645645 0
|
||||
177 1 6.138485 1
|
||||
179 1 2.8749352 1
|
||||
180 0 -4.7285223 0
|
||||
181 0 -5.232939 0
|
||||
183 1 9.06396 1
|
||||
187 1 12.00492 1
|
||||
188 1 8.1324835 1
|
||||
189 0 -3.6207743 0
|
||||
191 1 10.797473 1
|
||||
192 0 -3.7562728 0
|
||||
196 0 5.8725023 1
|
||||
198 0 -5.232939 0
|
||||
199 0 -4.3234034 0
|
||||
201 1 9.770606 1
|
||||
202 0 -4.54813 0
|
||||
204 0 -4.54813 0
|
||||
205 1 12.087651 1
|
||||
206 1 6.641531 1
|
||||
207 0 -4.7285223 0
|
||||
209 0 -3.5942607 0
|
||||
210 1 13.547517 1
|
||||
211 1 9.089206 1
|
||||
212 0 -4.54813 0
|
||||
216 0 -4.8905344 0
|
||||
218 1 7.7499733 1
|
||||
219 0 -2.4297438 0
|
||||
223 1 5.4305964 1
|
||||
226 1 9.166205 1
|
||||
228 0 -4.7285223 0
|
||||
233 1 5.6998806 1
|
||||
237 1 6.476473 1
|
||||
239 1 5.16975 1
|
||||
240 0 -1.9057708 0
|
||||
241 0 -3.8436408 0
|
||||
242 0 -4.2645645 0
|
||||
244 0 -4.54813 0
|
||||
246 1 11.068809 1
|
||||
247 1 3.1855068 1
|
||||
248 0 -2.7545462 0
|
||||
249 0 ? 0
|
||||
250 0 -4.655863 0
|
||||
252 0 4.2659445 1
|
||||
254 1 7.919628 1
|
||||
257 0 -4.3234034 0
|
||||
258 0 -3.9809995 0
|
||||
259 0 4.3407545 1
|
||||
260 1 8.330071 1
|
||||
262 1 9.629434 1
|
||||
267 1 3.677929 1
|
||||
268 1 8.609313 1
|
||||
269 0 -4.54813 0
|
||||
271 0 -3.413869 0
|
||||
272 1 3.677929 1
|
||||
275 0 ? 0
|
||||
276 0 -4.3234034 0
|
||||
277 0 -4.8905344 0
|
||||
278 0 -4.54813 0
|
||||
279 1 6.636299 1
|
||||
280 0 -3.9809995 0
|
||||
283 1 5.5930414 1
|
||||
284 1 6.3683033 1
|
||||
285 1 13.342931 1
|
||||
288 1 2.133194 1
|
||||
290 0 -5.232939 0
|
||||
291 0 -4.54813 0
|
||||
293 1 4.659646 1
|
||||
296 0 2.0631151 1
|
||||
297 0 ? 0
|
||||
299 1 6.110528 1
|
||||
300 1 6.6979437 1
|
||||
301 0 -4.54813 0
|
||||
303 0 -4.54813 0
|
||||
304 1 4.865142 1
|
||||
308 1 6.804653 1
|
||||
309 0 -1.4760427 0
|
||||
311 0 -5.232939 0
|
||||
312 1 3.2489738 1
|
||||
314 0 -5.070926 0
|
||||
316 1 4.409379 1
|
||||
317 1 8.798545 1
|
||||
319 0 2.7989082 1
|
||||
321 0 ? 0
|
||||
323 1 4.8855352 1
|
||||
327 0 -4.8905344 0
|
||||
328 1 4.1213074 1
|
||||
329 1 6.918497 1
|
||||
331 0 -3.1255894 0
|
||||
332 0 -2.8317442 0
|
||||
333 1 4.9879713 1
|
||||
336 1 5.3119774 1
|
||||
338 0 -5.070926 0
|
||||
343 0 -5.232939 0
|
||||
344 1 9.373975 1
|
||||
346 0 -2.8051786 0
|
||||
347 0 -5.0361757 0
|
||||
348 1 0.09843826 1
|
||||
349 1 3.543579 1
|
||||
350 0 -3.7809262 0
|
||||
352 0 1.515585 1
|
||||
353 1 9.391767 1
|
||||
354 0 -4.8905344 0
|
||||
355 0 -3.6708689 0
|
||||
358 1 6.2604895 1
|
||||
360 1 15.035466 1
|
||||
361 1 6.5571547 1
|
||||
366 1 13.467437 1
|
||||
368 0 -4.532379 0
|
||||
370 0 -3.0555735 0
|
||||
371 0 -4.532379 0
|
||||
373 0 -3.5973973 0
|
||||
376 0 -4.8905344 0
|
||||
377 0 -5.070926 0
|
||||
378 0 -3.477808 0
|
||||
379 0 -1.5486526 0
|
||||
381 1 8.643491 1
|
||||
383 0 -4.9493732 0
|
||||
384 0 -4.9493732 0
|
||||
387 0 -1.9204493 0
|
||||
388 0 -4.418391 0
|
||||
389 0 -2.9322662 0
|
||||
391 1 9.323798 1
|
||||
392 0 -4.3234034 0
|
||||
395 0 -4.3234034 0
|
||||
396 0 -3.9809995 0
|
||||
398 0 -3.9080298 0
|
||||
399 0 -4.305583 0
|
||||
404 0 -4.499814 0
|
||||
406 0 -3.4627624 0
|
||||
409 0 -3.973124 0
|
||||
413 0 -3.063589 0
|
||||
414 1 6.5001116 1
|
||||
415 0 -0.5379734 0
|
||||
416 1 8.981729 1
|
||||
418 0 -1.7460232 0
|
||||
419 0 -3.9546041 0
|
||||
422 0 -2.1661267 0
|
||||
423 0 -3.0557137 0
|
||||
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687 0 -4.295347 0
|
||||
690 0 -5.4890733 0
|
||||
695 0 -5.4523587 0
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 4.200075 0.9999651 5.0391796758373727E-05 1
|
||||
6 0 -0.30853248 0.2536451 0.42206626883841641 0
|
||||
8 0 -1.70133 0.010123582 0.014679672449874221 0
|
||||
9 0 -1.5288423 0.01553754 0.022591901886398588 0
|
||||
10 0 -1.9129735 0.005969637 0.0086381749163380664 0
|
||||
11 0 -1.8355699 0.007243505 0.010488200743506116 0
|
||||
18 1 2.5823843 0.99795955 0.0029467484071289771 1
|
||||
20 1 2.114814 0.9934155 0.0095308744102067192 1
|
||||
21 1 2.3608449 0.9964431 0.0051406802358066545 1
|
||||
25 1 0.44596696 0.69394016 0.5271168279878311 1
|
||||
28 0 -1.8355699 0.007243505 0.010488200743506116 0
|
||||
31 0 -1.7324895 0.009367555 0.013578222828773317 0
|
||||
32 1 2.35438 0.996385 0.0052248235129257635 1
|
||||
35 0 -1.8355699 0.007243505 0.010488200743506116 0
|
||||
37 0 -0.88281894 0.07420459 0.11123467943805206 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 0.6366718 0.7855495 0.34822586826299018 1
|
||||
44 1 2.7207205 0.99855834 0.002081372862402793 1
|
||||
45 0 -1.8799416 0.006483482 0.0093841415498267036 0
|
||||
46 1 1.8880317 0.9884103 0.016818058819898705 1
|
||||
48 0 -1.369495 0.023021998 0.033602016414606022 0
|
||||
50 1 0.630594 0.78296286 0.35298422271400048 1
|
||||
51 1 -0.08284855 0.37482342 1.4157169907406564 0
|
||||
52 1 1.3741143 0.95903736 0.060341072002409277 1
|
||||
54 1 2.382396 0.99663013 0.004869901644625689 1
|
||||
56 1 2.1698558 0.9942619 0.0083021409254577568 1
|
||||
60 1 0.58073115 0.76089483 0.39423102554115741 1
|
||||
63 1 0.08508468 0.47772494 1.0657479003557082 1
|
||||
64 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
66 0 -1.5250859 0.015682735 0.022804694445794606 0
|
||||
68 1 2.9279912 0.9991436 0.001236052716862961 1
|
||||
69 0 -1.7767313 0.008389268 0.012154209487592493 0
|
||||
70 0 -1.3055632 0.026930405 0.039385102809710824 0
|
||||
71 1 2.3767645 0.9965822 0.0049392741085566528 1
|
||||
72 0 -0.86906457 0.07661669 0.11499843805464016 0
|
||||
73 1 2.7834456 0.9987685 0.0017777616487900717 1
|
||||
74 1 0.68621945 0.8057985 0.31150902695404759 1
|
||||
76 0 -1.6109196 0.012675921 0.018404383255368032 0
|
||||
77 0 -1.2480209 0.03099459 0.045423375148012826 0
|
||||
79 0 -1.7587875 0.008773201 0.012712902109988501 0
|
||||
82 0 -1.3220601 0.025864072 0.037804999416162104 0
|
||||
88 0 -1.5250859 0.015682735 0.022804694445794606 0
|
||||
90 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
91 0 -1.7571532 0.0088090245 0.01276504261323873 0
|
||||
92 0 -1.5250859 0.015682735 0.022804694445794606 0
|
||||
93 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
95 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
96 0 -1.8608549 0.0068001845 0.0098441013606770621 0
|
||||
97 0 -1.4213842 0.020262035 0.029532148293535013 0
|
||||
98 1 2.8129733 0.99885654 0.0016506014616887051 1
|
||||
99 1 3.2547262 0.99962336 0.00054348154074645777 1
|
||||
100 1 1.6212642 0.9775762 0.032718938670381621 1
|
||||
102 0 -1.4156352 0.020551108 0.029957880523836078 0
|
||||
104 1 3.6374485 0.9998561 0.00020759799513193585 1
|
||||
105 1 0.37810874 0.65654033 0.60704444980350136 1
|
||||
106 1 3.151405 0.9995116 0.00070478504326742424 1
|
||||
108 0 -1.8203516 0.007524036 0.010895931525300316 0
|
||||
109 1 2.0232556 0.9917243 0.011988970794591214 1
|
||||
111 1 1.4585507 0.96661395 0.048988281724005323 1
|
||||
112 1 2.3923943 0.9967136 0.0047491117828311318 1
|
||||
113 1 3.449529 0.9997692 0.00033299683958315626 1
|
||||
115 0 -1.3633927 0.023369784 0.034115679937829324 0
|
||||
117 1 2.7568676 0.9986835 0.0019005416994463959 1
|
||||
120 0 -1.6489451 0.01153299 0.016735278332468032 0
|
||||
121 0 -1.256022 0.030395802 0.044532149932367517 0
|
||||
122 1 3.6395514 0.9998569 0.0002064799474635194 1
|
||||
123 1 1.5285003 0.9718489 0.041196061742169734 1
|
||||
125 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
128 1 1.6680334 0.98001516 0.029124030205691952 1
|
||||
129 0 -1.4280933 0.019929718 0.029042884913725323 0
|
||||
131 0 -1.7324895 0.009367555 0.013578222828773317 0
|
||||
132 1 3.34316 0.99969846 0.00043509571801397302 1
|
||||
133 0 -1.6724081 0.010879213 0.015781387675006649 0
|
||||
137 0 -1.8349485 0.007254753 0.010504546064679899 0
|
||||
138 0 -1.5187156 0.015932022 0.02317011608826015 0
|
||||
141 0 -1.9386503 0.0055983523 0.008099407969932049 0
|
||||
144 0 -1.8355699 0.007243505 0.010488200743506116 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -1.6927494 0.010342175 0.014998296674818341 0
|
||||
150 0 -1.9129735 0.005969637 0.0086381749163380664 0
|
||||
151 1 1.5516846 0.97340125 0.038893469912665984 1
|
||||
152 1 3.02924 0.999336 0.00095826154131685372 1
|
||||
154 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
156 0 -1.7304969 0.009414182 0.01364612858072025 0
|
||||
161 0 -1.4656259 0.018166868 0.026450243581314245 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 2.3187091 0.99604696 0.0057143326416850991 1
|
||||
169 0 -2.041339 0.0043294653 0.0062596584468447847 0
|
||||
171 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
173 1 4.862486 0.9999934 9.5450686985595153E-06 1
|
||||
174 1 1.9712131 0.99057776 0.013657868129433039 1
|
||||
176 0 -1.7324895 0.009367555 0.013578222828773317 0
|
||||
177 1 1.9338348 0.9896585 0.014997349031540621 1
|
||||
179 1 0.98654413 0.8982907 0.15474570718058153 1
|
||||
180 0 -1.9129735 0.005969637 0.0086381749163380664 0
|
||||
181 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
183 1 3.1711543 0.99953526 0.00067063020993693814 1
|
||||
187 1 3.8796742 0.99992174 0.00011291102860834543 1
|
||||
188 1 2.8745444 0.99902046 0.0014138740302619024 1
|
||||
189 0 -1.5179658 0.01596162 0.0232135081207458 0
|
||||
191 1 3.767329 0.9998962 0.00014971866598967564 1
|
||||
192 0 -1.5244646 0.01570688 0.022840084433316761 0
|
||||
196 0 1.8871028 0.9883835 6.4276772515227742 1
|
||||
198 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
199 0 -1.7318681 0.00938207 0.013599361228670748 0
|
||||
201 1 3.4492538 0.99976903 0.00033325487313387326 1
|
||||
202 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
204 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
205 1 4.0995226 0.999955 6.4924911571092269E-05 1
|
||||
206 1 2.3229492 0.99608874 0.005653814757257427 1
|
||||
207 0 -1.9129735 0.005969637 0.0086381749163380664 0
|
||||
209 0 -1.4981666 0.016763022 0.024388920908320399 0
|
||||
210 1 4.574052 0.99998635 1.9692147917725957E-05 1
|
||||
211 1 3.2051861 0.99957335 0.00061565725187682347 1
|
||||
212 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
216 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
218 1 2.6259325 0.9981709 0.0026412320631398097 1
|
||||
219 0 -1.098781 0.044486675 0.065652103229612402 0
|
||||
223 1 1.7418234 0.9833443 0.02423143243676934 1
|
||||
226 1 3.0568173 0.99938047 0.00089407081728932005 1
|
||||
228 0 -1.9129735 0.005969637 0.0086381749163380664 0
|
||||
233 1 2.0051677 0.9913424 0.01254462171484411 1
|
||||
237 1 2.059108 0.9924326 0.010958976684646626 1
|
||||
239 1 1.6806457 0.9806272 0.028223346851889448 1
|
||||
240 0 -0.8613124 0.078007676 0.11717335480868181 0
|
||||
241 0 -1.5499792 0.014744883 0.021430758344919889 0
|
||||
242 0 -1.7324895 0.009367555 0.013578222828773317 0
|
||||
244 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
246 1 3.89981 0.9999256 0.00010732116581602008 1
|
||||
247 1 0.9928844 0.89973855 0.15242225694242206 1
|
||||
248 0 -1.2277858 0.032560337 0.047756408915339651 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -1.8335773 0.0072796373 0.010540709578550534 0
|
||||
252 0 1.3719904 0.958827 4.6021582743561993 1
|
||||
254 1 2.7937534 0.9988 0.0017323029622904203 1
|
||||
257 0 -1.7318681 0.00938207 0.013599361228670748 0
|
||||
258 0 -1.6287878 0.012125563 0.017600413857865052 0
|
||||
259 0 1.4281704 0.9640579 4.7981823833320787 1
|
||||
260 1 2.8899968 0.99905777 0.0013599916857192891 1
|
||||
262 1 3.2805288 0.999647 0.00050933052590907497 1
|
||||
267 1 1.1022213 0.921965 0.11721610663727763 1
|
||||
268 1 2.5098183 0.997552 0.0035360796959713393 1
|
||||
269 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
271 0 -1.4213842 0.020262035 0.029532148293535013 0
|
||||
272 1 1.1022213 0.921965 0.11721610663727763 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -1.7318681 0.00938207 0.013599361228670748 0
|
||||
277 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
278 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
279 1 1.936168 0.9897184 0.014910027325241739 1
|
||||
280 0 -1.6287878 0.012125563 0.017600413857865052 0
|
||||
283 1 1.79509 0.9854024 0.021215101768506751 1
|
||||
284 1 2.2251618 0.99500346 0.0072265497662589986 1
|
||||
285 1 4.654189 0.99998885 1.6080467472835657E-05 1
|
||||
288 1 0.475044 0.70925033 0.49563317624888092 1
|
||||
290 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
291 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
293 1 1.655925 0.9794099 0.030015354878194605 1
|
||||
296 0 0.5535717 0.7482451 1.9899083515567451 1
|
||||
297 0 ? ? ? 0
|
||||
299 1 2.001486 0.99126256 0.012660861084033199 1
|
||||
300 1 2.0941775 0.9930671 0.010036913891508983 1
|
||||
301 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
303 0 -1.8361913 0.0072322735 0.010471878615362695 0
|
||||
304 1 1.7624457 0.98417294 0.023016244643388774 1
|
||||
308 1 2.1296265 0.9936548 0.0091833726838673919 1
|
||||
309 0 -0.74366736 0.102127604 0.15541766886087094 0
|
||||
311 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
312 1 1.2669513 0.9470354 0.078509782787093155 1
|
||||
314 0 -2.0160542 0.0046124593 0.0066697660741590094 0
|
||||
316 1 1.4715164 0.9676506 0.047441894201880302 1
|
||||
317 1 3.0652492 0.99939346 0.00087531320929783803 1
|
||||
319 0 0.8519552 0.8629285 2.8669995609516308 1
|
||||
321 0 ? ? ? 0
|
||||
323 1 1.75474 0.9838682 0.023463058122578043 1
|
||||
327 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
328 1 1.431824 0.964375 0.052333814670251486 1
|
||||
329 1 2.3460639 0.99630886 0.0053350370543586998 1
|
||||
331 0 -1.3620541 0.023446757 0.03422939146313124 0
|
||||
332 0 -1.1784344 0.036705967 0.05395186572045025 0
|
||||
333 1 1.7160428 0.98224825 0.025840407436881135 1
|
||||
336 1 1.9215882 0.9893384 0.01546402326222397 1
|
||||
338 0 -2.0160542 0.0046124593 0.0066697660741590094 0
|
||||
343 0 -2.0423522 0.004318492 0.006243758558693949 0
|
||||
344 1 3.2724602 0.99963975 0.00051982521016927393 1
|
||||
346 0 -1.1452723 0.039772168 0.058551341777431119 0
|
||||
347 0 -2.0002143 0.0047990466 0.0069402272528187436 0
|
||||
348 1 0.046962738 0.45386875 1.1396529466651428 1
|
||||
349 1 1.0789044 0.9176395 0.12400061058625531 1
|
||||
350 0 -1.4719541 0.017885108 0.026036287173924786 0
|
||||
352 0 0.3863504 0.6611998 1.5614934031476035 1
|
||||
353 1 3.2169917 0.99958587 0.00059759147883748926 1
|
||||
354 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
355 0 -1.4919239 0.01702381 0.024771622277675296 0
|
||||
358 1 2.1038258 0.9932322 0.0097970749358899187 1
|
||||
360 1 4.9878798 0.9999952 6.9653141741429021E-06 1
|
||||
361 1 2.4031284 0.99680084 0.0046228109150398015 1
|
||||
366 1 4.5532446 0.99998564 2.0724058276965695E-05 1
|
||||
368 0 -1.8234507 0.007466047 0.010811639599453086 0
|
||||
370 0 -1.2432852 0.031354368 0.045959125919956823 0
|
||||
371 0 -1.8234507 0.007466047 0.010811639599453086 0
|
||||
373 0 -1.4466691 0.019037232 0.027729713954362165 0
|
||||
376 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
377 0 -2.0160542 0.0046124593 0.0066697660741590094 0
|
||||
378 0 -1.3622394 0.02343609 0.034213632285549529 0
|
||||
379 0 -0.740819 0.102786474 0.1564767247449558 0
|
||||
381 1 2.8893797 0.99905634 0.0013620574254095477 1
|
||||
383 0 -1.9386503 0.0055983523 0.008099407969932049 0
|
||||
384 0 -1.9386503 0.0055983523 0.008099407969932049 0
|
||||
387 0 -0.84925675 0.08021681 0.12063426385787543 0
|
||||
388 0 -1.7761098 0.008402282 0.012173144127031552 0
|
||||
389 0 -1.2184823 0.03330563 0.048868255554553125 0
|
||||
391 1 3.1749265 0.9995396 0.00066434993816633352 1
|
||||
392 0 -1.7318681 0.00938207 0.013599361228670748 0
|
||||
395 0 -1.7318681 0.00938207 0.013599361228670748 0
|
||||
396 0 -1.6287878 0.012125563 0.017600413857865052 0
|
||||
398 0 -1.573084 0.013923961 0.020229193480366881 0
|
||||
399 0 -1.7241267 0.009564792 0.0138654942043478 0
|
||||
404 0 -1.727977 0.009473476 0.013732486690151677 0
|
||||
406 0 -1.4193915 0.02036178 0.029679032908949233 0
|
||||
409 0 -1.6224174 0.0123190135 0.017882957408602049 0
|
||||
413 0 -1.3119335 0.026513664 0.038767365760229225 0
|
||||
414 1 2.123049 0.9935496 0.0093361246520084814 1
|
||||
415 0 -0.27760935 0.26865032 0.45136673462911298 0
|
||||
416 1 3.1767018 0.9995417 0.00066133885865735131 1
|
||||
418 0 -0.8462839 0.080770284 0.12150265795304209 0
|
||||
419 0 -1.5096551 0.016293311 0.023699883097952418 0
|
||||
422 0 -0.9886162 0.057869673 0.086001449661397389 0
|
||||
423 0 -1.3055632 0.026930405 0.039385102809710824 0
|
||||
428 0 -1.9392717 0.0055896565 0.0080867920683108836 0
|
||||
429 0 -1.8355699 0.007243505 0.010488200743506116 0
|
||||
430 0 -1.6811634 0.01064476 0.015439463700878843 0
|
||||
434 0 2.025863 0.99177796 6.9262872774368329 1
|
||||
436 1 1.5963671 0.9761615 0.034808271840087598 1
|
||||
439 0 -1.627545 0.012163065 0.01765518366670454 0
|
||||
440 1 2.716944 0.99854463 0.0021011795574893714 1
|
||||
441 0 -0.6761644 0.11878278 0.18243040939221555 0
|
||||
442 0 -1.5472815 0.01484379 0.0215755929323194 0
|
||||
449 1 3.4800646 0.99978626 0.0003083978529977051 1
|
||||
450 0 -1.5493578 0.01476761 0.02146403628241873 0
|
||||
451 0 -1.627545 0.012163065 0.01765518366670454 0
|
||||
452 0 -1.6793576 0.010692704 0.015509377521470026 0
|
||||
453 1 2.7676923 0.9987188 0.0018495686286770713 1
|
||||
454 0 -1.6632198 0.011130753 0.016148321260679364 0
|
||||
455 1 0.0077679157 0.42956108 1.2190648146203023 1
|
||||
456 1 3.1468236 0.99950594 0.0007129582341136991 1
|
||||
457 1 2.752885 0.9986703 0.0019196570653076585 1
|
||||
464 0 -1.7312468 0.009396606 0.013620531134564339 0
|
||||
465 1 3.0071166 0.99929804 0.0010130754524185037 1
|
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|
||||
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||||
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||||
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||||
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|
||||
315 0 ? ? ? 0
|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
411 0 ? ? ? 0
|
||||
412 1 2.820312 0.99645597 0.0051220399276606741 1
|
||||
417 0 -2.0245085 0.005386469 0.0077920367211286434 0
|
||||
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|
||||
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|
||||
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|
||||
425 1 4.6287465 0.9999382 8.9175760478377495E-05 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
479 1 2.6295075 0.9945752 0.0078476337111689238 1
|
||||
481 0 -1.050567 0.04583318 0.067686578076222359 0
|
||||
485 0 -1.6411834 0.012624384 0.018329077691491284 0
|
||||
486 0 -1.7818983 0.009241497 0.013394651189791419 0
|
||||
488 1 0.43259048 0.57149994 0.80717473848753052 1
|
||||
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|
||||
491 1 2.140998 0.9839611 0.023326805981877335 1
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
506 1 3.383957 0.9989953 0.0014501984077122476 1
|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
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||||
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|
||||
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|
||||
555 0 -0.54130745 0.13072506 0.2021155338370372 0
|
||||
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|
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|
||||
568 1 1.5344815 0.9403269 0.088765660460296075 1
|
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|
||||
571 1 3.5222406 0.99926287 0.0010638468668610122 1
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574 1 1.9515014 0.97568154 0.035517757344877311 1
|
||||
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|
||||
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|
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|
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|
||||
587 0 -1.406426 0.021179358 0.030883567994507369 0
|
||||
588 1 1.5752573 0.94525284 0.081227822595337187 1
|
||||
589 0 -1.5780818 0.014514192 0.021092999758877816 0
|
||||
591 1 1.8975711 0.9726429 0.040017871628344054 1
|
||||
592 1 1.5472884 0.9419172 0.086327879838672378 1
|
||||
595 0 -1.5152345 0.016672775 0.024256508319599631 0
|
||||
596 0 -1.527883 0.016214361 0.023584100298208441 0
|
||||
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|
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598 0 -1.7698715 0.009491568 0.013758838974379935 0
|
||||
599 0 -1.1391528 0.037893206 0.055731052457108757 0
|
||||
601 0 -2.0365353 0.005243981 0.0075853712813971049 0
|
||||
603 1 1.2235928 0.8870178 0.17296506111393734 1
|
||||
605 1 3.0743294 0.9979912 0.0029009943787648728 1
|
||||
608 1 3.1401954 0.9982664 0.00250322812715321 1
|
||||
610 1 2.1142316 0.98298645 0.024756564761747188 1
|
||||
611 1 2.0873475 0.981949 0.026280039918063384 1
|
||||
615 0 -1.4132128 0.020866338 0.030422278872440509 0
|
||||
616 0 -1.7698715 0.009491568 0.013758838974379935 0
|
||||
620 0 -1.7698715 0.009491568 0.013758838974379935 0
|
||||
623 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
625 0 -1.1030068 0.040959504 0.06033635903518602 0
|
||||
626 1 1.6810002 0.956299 0.064466317286346234 1
|
||||
628 0 -1.9600375 0.006218537 0.0089994636539313857 0
|
||||
630 0 -1.1144776 0.03996154 0.05883589105238779 0
|
||||
631 0 -1.5152345 0.016672775 0.024256508319599631 0
|
||||
632 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
635 0 -1.4887085 0.017675899 0.025728999261244733 0
|
||||
636 1 2.8873758 0.996949 0.0044083663249413997 1
|
||||
637 0 -0.8961797 0.06357536 0.09476519420568516 0
|
||||
640 0 -1.5727646 0.014685623 0.021343987298884801 0
|
||||
643 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
646 0 -1.8657403 0.007670605 0.01110900499779861 0
|
||||
647 0 -1.9547201 0.0062926137 0.0091070061446696399 0
|
||||
648 1 3.3080468 0.9988092 0.0017189583546918133 1
|
||||
650 0 -1.2660291 0.028785164 0.04213763424044846 0
|
||||
651 0 -1.7849274 0.009179549 0.013304448516533519 0
|
||||
655 0 -1.7698715 0.009491568 0.013758838974379935 0
|
||||
658 1 2.6977997 0.9953414 0.0067366137513617651 1
|
||||
659 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
662 0 -1.7951683 0.008973145 0.01300394246354388 0
|
||||
663 0 -1.7951683 0.008973145 0.01300394246354388 0
|
||||
664 0 -1.6372358 0.012735136 0.018490910986938239 0
|
||||
666 0 -1.2356119 0.030753305 0.045064184567971469 0
|
||||
667 0 -1.7070242 0.010911443 0.015828398337213719 0
|
||||
669 1 2.924252 0.9971903 0.0040592504084616312 1
|
||||
671 0 -1.5337609 0.016005576 0.023277954114855396 0
|
||||
672 0 -1.8343427 0.008225174 0.011915488227056287 0
|
||||
673 0 -1.237494 0.030627832 0.044877433262258515 0
|
||||
674 0 -2.0245085 0.005386469 0.0077920367211286434 0
|
||||
675 0 -1.3613901 0.023375936 0.034124768275389884 0
|
||||
676 0 -1.9098384 0.006953827 0.010067295706409204 0
|
||||
677 0 -1.5780818 0.014514192 0.021092999758877816 0
|
||||
684 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
686 0 -2.0873559 0.0046821386 0.0067707613040768992 0
|
||||
687 0 -1.5522544 0.015365773 0.022340204555745057 0
|
||||
690 0 -1.9547201 0.0062926137 0.0091070061446696399 0
|
||||
695 0 -1.9600375 0.006218537 0.0089994636539313857 0
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
maml.exe TrainTest test=%Data% tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 232 | 7 | 0.9707
|
||||
negative || 12 | 432 | 0.9730
|
||||
||======================
|
||||
Precision || 0.9508 | 0.9841 |
|
||||
OVERALL 0/1 ACCURACY: 0.972182
|
||||
LOG LOSS/instance: 0.115962
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.875844
|
||||
AUC: 0.995995
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995995 (0.0000)
|
||||
Accuracy: 0.972182 (0.0000)
|
||||
Positive precision: 0.950820 (0.0000)
|
||||
Positive recall: 0.970711 (0.0000)
|
||||
Negative precision: 0.984055 (0.0000)
|
||||
Negative recall: 0.972973 (0.0000)
|
||||
Log-loss: 0.115962 (0.0000)
|
||||
Log-loss reduction: 0.875844 (0.0000)
|
||||
F1 Score: 0.960663 (0.0000)
|
||||
AUPRC: 0.991840 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lr /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995995 0.972182 0.95082 0.970711 0.984055 0.972973 0.115962 0.875844 0.960663 0.99184 0.01 100 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=%Output% data=%Data% out=%Output% seed=1 /lr:0.01;/iter:100
|
||||
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)
|
||||
Training calibrator.
|
||||
PAV calibrator: piecewise function approximation has 9 components.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 234 | 5 | 0.9791
|
||||
negative || 12 | 432 | 0.9730
|
||||
||======================
|
||||
Precision || 0.9512 | 0.9886 |
|
||||
OVERALL 0/1 ACCURACY: 0.975110
|
||||
LOG LOSS/instance: 0.084507
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.909522
|
||||
AUC: 0.996146
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996146 (0.0000)
|
||||
Accuracy: 0.975110 (0.0000)
|
||||
Positive precision: 0.951220 (0.0000)
|
||||
Positive recall: 0.979079 (0.0000)
|
||||
Negative precision: 0.988558 (0.0000)
|
||||
Negative recall: 0.972973 (0.0000)
|
||||
Log-loss: 0.084507 (0.0000)
|
||||
Log-loss reduction: 0.909522 (0.0000)
|
||||
F1 Score: 0.964948 (0.0000)
|
||||
AUPRC: 0.992065 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.084507 0.909522 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.5726995 1E-15 1.4415419267167138E-15 0
|
||||
1 0 3.6101446 0.8333333 2.5849623287385155 1
|
||||
2 0 -4.0709443 1E-15 1.4415419267167138E-15 0
|
||||
3 0 2.470542 0.8095238 2.3923175087700885 1
|
||||
4 0 -3.4358397 1E-15 1.4415419267167138E-15 0
|
||||
5 1 12.382593 1 -0 1
|
||||
6 0 -1.4209604 0.071428575 0.10691520887754996 0
|
||||
7 0 -4.7010875 1E-15 1.4415419267167138E-15 0
|
||||
8 0 -4.6745405 1E-15 1.4415419267167138E-15 0
|
||||
9 0 -4.406417 1E-15 1.4415419267167138E-15 0
|
||||
10 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
11 0 -5.4818344 1E-15 1.4415419267167138E-15 0
|
||||
12 1 -0.14206886 0.6363636 0.65207672114864346 0
|
||||
13 0 -4.5691886 1E-15 1.4415419267167138E-15 0
|
||||
14 1 9.321613 1 -0 1
|
||||
15 1 1.3856993 0.8095238 0.30485456129516797 1
|
||||
16 0 -4.533843 1E-15 1.4415419267167138E-15 0
|
||||
17 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
18 1 7.8903713 1 -0 1
|
||||
19 0 -3.0987039 1E-15 1.4415419267167138E-15 0
|
||||
20 1 7.528511 1 -0 1
|
||||
21 1 7.875206 1 -0 1
|
||||
22 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
25 1 1.741828 0.8095238 0.30485456129516797 1
|
||||
26 0 -4.9710746 1E-15 1.4415419267167138E-15 0
|
||||
27 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
28 0 -5.4818344 1E-15 1.4415419267167138E-15 0
|
||||
29 0 -5.8557806 1E-15 1.4415419267167138E-15 0
|
||||
30 0 -5.0985007 1E-15 1.4415419267167138E-15 0
|
||||
31 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
32 1 7.46414 1 -0 1
|
||||
33 0 -4.6892586 1E-15 1.4415419267167138E-15 0
|
||||
34 0 -4.71424 1E-15 1.4415419267167138E-15 0
|
||||
35 0 -5.4818344 1E-15 1.4415419267167138E-15 0
|
||||
36 1 9.09911 1 -0 1
|
||||
37 0 -1.113348 0.071428575 0.10691520887754996 0
|
||||
38 1 6.140955 0.98 0.029146317580716615 1
|
||||
39 1 2.5109034 0.8095238 0.30485456129516797 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 3.3300762 0.8333333 0.26303444023032446 1
|
||||
42 1 8.577511 1 -0 1
|
||||
43 1 0.49126053 0.6363636 0.65207672114864346 1
|
||||
44 1 8.255751 1 -0 1
|
||||
45 0 -5.63222 1E-15 1.4415419267167138E-15 0
|
||||
46 1 4.5673847 0.9285714 0.10691524360481655 1
|
||||
47 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
48 0 -3.4358397 1E-15 1.4415419267167138E-15 0
|
||||
49 1 5.3666534 0.98 0.029146317580716615 1
|
||||
50 1 2.5949688 0.8095238 0.30485456129516797 1
|
||||
51 1 0.12595844 0.6363636 0.65207672114864346 1
|
||||
52 1 5.2992115 0.98 0.029146317580716615 1
|
||||
53 1 8.407227 1 -0 1
|
||||
54 1 7.649309 1 -0 1
|
||||
55 1 4.478711 0.9285714 0.10691524360481655 1
|
||||
56 1 5.5541334 0.98 0.029146317580716615 1
|
||||
57 1 1.6657066 0.8095238 0.30485456129516797 1
|
||||
58 1 2.5265894 0.8095238 0.30485456129516797 1
|
||||
59 1 1.7368536 0.8095238 0.30485456129516797 1
|
||||
60 1 2.3288136 0.8095238 0.30485456129516797 1
|
||||
61 0 -5.5060835 1E-15 1.4415419267167138E-15 0
|
||||
62 1 6.380089 0.98 0.029146317580716615 1
|
||||
63 1 0.33490086 0.6363636 0.65207672114864346 1
|
||||
64 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
65 1 3.8072634 0.9285714 0.10691524360481655 1
|
||||
66 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
67 1 4.218014 0.9285714 0.10691524360481655 1
|
||||
68 1 10.826725 1 -0 1
|
||||
69 0 -5.271654 1E-15 1.4415419267167138E-15 0
|
||||
70 0 -3.4726496 1E-15 1.4415419267167138E-15 0
|
||||
71 1 7.895046 1 -0 1
|
||||
72 0 -2.1755848 0.071428575 0.10691520887754996 0
|
||||
73 1 8.9055195 1 -0 1
|
||||
74 1 2.5993576 0.8095238 0.30485456129516797 1
|
||||
75 0 -4.0411606 1E-15 1.4415419267167138E-15 0
|
||||
76 0 -5.0750337 1E-15 1.4415419267167138E-15 0
|
||||
77 0 -3.499567 1E-15 1.4415419267167138E-15 0
|
||||
78 0 -3.6211972 1E-15 1.4415419267167138E-15 0
|
||||
79 0 -5.391173 1E-15 1.4415419267167138E-15 0
|
||||
80 0 -2.7157316 1E-15 1.4415419267167138E-15 0
|
||||
81 0 -4.2284155 1E-15 1.4415419267167138E-15 0
|
||||
82 0 -3.4452734 1E-15 1.4415419267167138E-15 0
|
||||
83 0 -2.1223526 0.071428575 0.10691520887754996 0
|
||||
84 1 9.694054 1 -0 1
|
||||
85 1 6.2895603 0.98 0.029146317580716615 1
|
||||
86 1 2.6168842 0.8095238 0.30485456129516797 1
|
||||
87 1 6.919142 1 -0 1
|
||||
88 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
89 0 -5.085745 1E-15 1.4415419267167138E-15 0
|
||||
90 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
91 0 -5.189559 1E-15 1.4415419267167138E-15 0
|
||||
92 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
93 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
94 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
95 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
96 0 -5.663555 1E-15 1.4415419267167138E-15 0
|
||||
97 0 -3.5726995 1E-15 1.4415419267167138E-15 0
|
||||
98 1 8.590233 1 -0 1
|
||||
99 1 10.917194 1 -0 1
|
||||
100 1 4.8476696 0.9285714 0.10691524360481655 1
|
||||
101 1 -0.84280396 0.5 1 0
|
||||
102 0 -3.7530966 1E-15 1.4415419267167138E-15 0
|
||||
103 1 1.7746439 0.8095238 0.30485456129516797 1
|
||||
104 1 12.140858 1 -0 1
|
||||
105 1 2.5560713 0.8095238 0.30485456129516797 1
|
||||
106 1 9.259369 1 -0 1
|
||||
107 1 6.720646 0.98 0.029146317580716615 1
|
||||
108 0 -5.5617743 1E-15 1.4415419267167138E-15 0
|
||||
109 1 6.871727 1 -0 1
|
||||
110 0 -2.766693 1E-15 1.4415419267167138E-15 0
|
||||
111 1 3.848031 0.9285714 0.10691524360481655 1
|
||||
112 1 9.425768 1 -0 1
|
||||
113 1 9.506624 1 -0 1
|
||||
114 0 -3.0727458 1E-15 1.4415419267167138E-15 0
|
||||
115 0 -4.6439905 1E-15 1.4415419267167138E-15 0
|
||||
116 0 -0.66188717 0.5 1 0
|
||||
117 1 9.617275 1 -0 1
|
||||
118 0 -5.3621607 1E-15 1.4415419267167138E-15 0
|
||||
119 0 -3.9435177 1E-15 1.4415419267167138E-15 0
|
||||
120 0 -4.8696556 1E-15 1.4415419267167138E-15 0
|
||||
121 0 -3.469522 1E-15 1.4415419267167138E-15 0
|
||||
122 1 9.680523 1 -0 1
|
||||
123 1 3.8165932 0.9285714 0.10691524360481655 1
|
||||
124 1 7.6522446 1 -0 1
|
||||
125 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
126 1 8.564951 1 -0 1
|
||||
127 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
128 1 4.84898 0.9285714 0.10691524360481655 1
|
||||
129 0 -5.717684 1E-15 1.4415419267167138E-15 0
|
||||
130 0 -3.4726496 1E-15 1.4415419267167138E-15 0
|
||||
131 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
132 1 8.602232 1 -0 1
|
||||
133 0 -4.810811 1E-15 1.4415419267167138E-15 0
|
||||
134 0 -4.9171767 1E-15 1.4415419267167138E-15 0
|
||||
135 0 -2.7288966 1E-15 1.4415419267167138E-15 0
|
||||
136 0 -4.533843 1E-15 1.4415419267167138E-15 0
|
||||
137 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
138 0 -4.2402444 1E-15 1.4415419267167138E-15 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
141 0 -5.9689827 1E-15 1.4415419267167138E-15 0
|
||||
142 1 4.4324036 0.9285714 0.10691524360481655 1
|
||||
143 0 -4.6439905 1E-15 1.4415419267167138E-15 0
|
||||
144 0 -5.4818344 1E-15 1.4415419267167138E-15 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 1.3394346 0.8095238 0.30485456129516797 1
|
||||
147 0 -5.4154215 1E-15 1.4415419267167138E-15 0
|
||||
148 0 -1.0123739 0.071428575 0.10691520887754996 0
|
||||
149 1 11.461615 1 -0 1
|
||||
150 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
151 1 5.006485 0.9285714 0.10691524360481655 1
|
||||
152 1 9.715748 1 -0 1
|
||||
153 0 -4.121497 1E-15 1.4415419267167138E-15 0
|
||||
154 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
155 1 3.7769232 0.9285714 0.10691524360481655 1
|
||||
156 0 -5.5348053 1E-15 1.4415419267167138E-15 0
|
||||
157 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
158 0 ? ? ? 0
|
||||
159 1 12.346203 1 -0 1
|
||||
160 1 9.039494 1 -0 1
|
||||
161 0 -3.8496675 1E-15 1.4415419267167138E-15 0
|
||||
162 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
163 0 -3.3870554 1E-15 1.4415419267167138E-15 0
|
||||
164 0 ? ? ? 0
|
||||
165 0 -3.3999205 1E-15 1.4415419267167138E-15 0
|
||||
166 1 7.976185 1 -0 1
|
||||
167 1 8.355644 1 -0 1
|
||||
168 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
169 0 -6.2282124 1E-15 1.4415419267167138E-15 0
|
||||
170 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
171 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
172 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
173 1 15.1560135 1 -0 1
|
||||
174 1 6.1769257 0.98 0.029146317580716615 1
|
||||
175 1 7.842922 1 -0 1
|
||||
176 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
177 1 4.766121 0.9285714 0.10691524360481655 1
|
||||
178 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
179 1 2.290575 0.8095238 0.30485456129516797 1
|
||||
180 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
181 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
182 0 -3.0987039 1E-15 1.4415419267167138E-15 0
|
||||
183 1 9.1599655 1 -0 1
|
||||
184 1 6.2014637 0.98 0.029146317580716615 1
|
||||
185 0 -5.0853486 1E-15 1.4415419267167138E-15 0
|
||||
186 1 5.7654095 0.98 0.029146317580716615 1
|
||||
187 1 13.977451 1 -0 1
|
||||
188 1 9.065283 1 -0 1
|
||||
189 0 -4.7540584 1E-15 1.4415419267167138E-15 0
|
||||
190 1 11.957216 1 -0 1
|
||||
191 1 10.956871 1 -0 1
|
||||
192 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
193 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
194 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
195 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
196 0 6.8652763 0.98 5.6438575656365879 1
|
||||
197 0 -2.6564164 1E-15 1.4415419267167138E-15 0
|
||||
198 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
199 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
200 1 10.36586 1 -0 1
|
||||
201 1 9.869495 1 -0 1
|
||||
202 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
203 0 -3.5726995 1E-15 1.4415419267167138E-15 0
|
||||
204 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
205 1 12.086603 1 -0 1
|
||||
206 1 5.94417 0.98 0.029146317580716615 1
|
||||
207 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
208 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
209 0 -3.6633615 1E-15 1.4415419267167138E-15 0
|
||||
210 1 14.534115 1 -0 1
|
||||
211 1 9.64962 1 -0 1
|
||||
212 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
213 1 14.52906 1 -0 1
|
||||
214 1 13.868914 1 -0 1
|
||||
215 1 7.643734 1 -0 1
|
||||
216 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
217 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
218 1 7.88678 1 -0 1
|
||||
219 0 -2.511506 1E-15 1.4415419267167138E-15 0
|
||||
220 0 -5.1632547 1E-15 1.4415419267167138E-15 0
|
||||
221 1 10.395218 1 -0 1
|
||||
222 1 -2.214662 0.071428575 3.8073548575641118 0
|
||||
223 1 5.7424126 0.98 0.029146317580716615 1
|
||||
224 1 9.995327 1 -0 1
|
||||
225 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
226 1 10.225868 1 -0 1
|
||||
227 1 7.459608 1 -0 1
|
||||
228 0 -5.5593443 1E-15 1.4415419267167138E-15 0
|
||||
229 1 12.666517 1 -0 1
|
||||
230 1 6.1583214 0.98 0.029146317580716615 1
|
||||
231 1 8.623034 1 -0 1
|
||||
232 0 1.2822819 0.6363636 1.4594315756416352 1
|
||||
233 1 6.382519 0.98 0.029146317580716615 1
|
||||
234 0 -2.8964381 1E-15 1.4415419267167138E-15 0
|
||||
235 0 ? ? ? 0
|
||||
236 1 11.420414 1 -0 1
|
||||
237 1 6.5357933 0.98 0.029146317580716615 1
|
||||
238 1 12.422876 1 -0 1
|
||||
239 1 5.902529 0.98 0.029146317580716615 1
|
||||
240 0 -2.017991 0.071428575 0.10691520887754996 0
|
||||
241 0 -4.0004973 1E-15 1.4415419267167138E-15 0
|
||||
242 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
243 0 -2.6953988 1E-15 1.4415419267167138E-15 0
|
||||
244 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
245 0 -2.817525 1E-15 1.4415419267167138E-15 0
|
||||
246 1 11.424004 1 -0 1
|
||||
247 1 3.104393 0.8333333 0.26303444023032446 1
|
||||
248 0 -3.0615559 1E-15 1.4415419267167138E-15 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -6.021953 1E-15 1.4415419267167138E-15 0
|
||||
251 1 8.872498 1 -0 1
|
||||
252 0 4.5387735 0.9285714 3.8073544061097437 1
|
||||
253 1 8.577511 1 -0 1
|
||||
254 1 6.380089 0.98 0.029146317580716615 1
|
||||
255 1 4.052039 0.9285714 0.10691524360481655 1
|
||||
256 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
257 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
258 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
259 0 2.9647493 0.8095238 2.3923175087700885 1
|
||||
260 1 9.870926 1 -0 1
|
||||
261 1 12.206299 1 -0 1
|
||||
262 1 9.653841 1 -0 1
|
||||
263 1 8.981979 1 -0 1
|
||||
264 1 5.664709 0.98 0.029146317580716615 1
|
||||
265 0 -2.494875 1E-15 1.4415419267167138E-15 0
|
||||
266 1 7.3661613 1 -0 1
|
||||
267 1 3.3009596 0.8333333 0.26303444023032446 1
|
||||
268 1 9.372967 1 -0 1
|
||||
269 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
270 1 6.031377 0.98 0.029146317580716615 1
|
||||
271 0 -3.5726995 1E-15 1.4415419267167138E-15 0
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546 1 11.390259 1 -0 1
|
||||
547 0 -6.059644 1E-15 1.4415419267167138E-15 0
|
||||
548 0 -5.5856485 1E-15 1.4415419267167138E-15 0
|
||||
549 1 6.3187475 0.98 0.029146317580716615 1
|
||||
550 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
551 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
552 0 -3.1100621 1E-15 1.4415419267167138E-15 0
|
||||
553 0 -1.7353673 0.071428575 0.10691520887754996 0
|
||||
554 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
555 0 -1.9254994 0.071428575 0.10691520887754996 0
|
||||
556 0 -3.424057 1E-15 1.4415419267167138E-15 0
|
||||
557 0 -3.93614 1E-15 1.4415419267167138E-15 0
|
||||
558 0 -5.4818344 1E-15 1.4415419267167138E-15 0
|
||||
559 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
560 0 -3.5726995 1E-15 1.4415419267167138E-15 0
|
||||
561 0 -3.5726995 1E-15 1.4415419267167138E-15 0
|
||||
562 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
563 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
564 0 -3.8496675 1E-15 1.4415419267167138E-15 0
|
||||
565 1 12.086092 1 -0 1
|
||||
566 0 -4.227092 1E-15 1.4415419267167138E-15 0
|
||||
567 0 -3.6343493 1E-15 1.4415419267167138E-15 0
|
||||
568 1 4.1473064 0.9285714 0.10691524360481655 1
|
||||
569 1 10.713882 1 -0 1
|
||||
570 1 8.017664 1 -0 1
|
||||
571 1 11.034657 1 -0 1
|
||||
572 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
573 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
574 1 5.950968 0.98 0.029146317580716615 1
|
||||
575 0 -3.2791004 1E-15 1.4415419267167138E-15 0
|
||||
576 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
577 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
578 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
579 0 -5.4686823 1E-15 1.4415419267167138E-15 0
|
||||
580 0 -3.7662487 1E-15 1.4415419267167138E-15 0
|
||||
581 1 8.417797 1 -0 1
|
||||
582 1 7.907978 1 -0 1
|
||||
583 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
584 0 -2.9291954 1E-15 1.4415419267167138E-15 0
|
||||
585 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
586 1 13.98102 1 -0 1
|
||||
587 0 -3.8628197 1E-15 1.4415419267167138E-15 0
|
||||
588 1 5.463169 0.98 0.029146317580716615 1
|
||||
589 0 -4.5469956 1E-15 1.4415419267167138E-15 0
|
||||
590 1 3.9684029 0.9285714 0.10691524360481655 1
|
||||
591 1 5.966527 0.98 0.029146317580716615 1
|
||||
592 1 5.7801704 0.98 0.029146317580716615 1
|
||||
593 0 -4.253397 1E-15 1.4415419267167138E-15 0
|
||||
594 1 5.101775 0.98 0.029146317580716615 1
|
||||
595 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
596 0 -4.2402444 1E-15 1.4415419267167138E-15 0
|
||||
597 0 -2.9855018 1E-15 1.4415419267167138E-15 0
|
||||
598 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
599 0 -3.6294346 1E-15 1.4415419267167138E-15 0
|
||||
600 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
601 0 -6.046492 1E-15 1.4415419267167138E-15 0
|
||||
602 0 -4.533843 1E-15 1.4415419267167138E-15 0
|
||||
603 1 4.8058815 0.9285714 0.10691524360481655 1
|
||||
604 1 6.1928043 0.98 0.029146317580716615 1
|
||||
605 1 9.955452 1 -0 1
|
||||
606 0 -4.715564 1E-15 1.4415419267167138E-15 0
|
||||
607 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
608 1 11.148428 1 -0 1
|
||||
609 0 -4.5469956 1E-15 1.4415419267167138E-15 0
|
||||
610 1 8.926189 1 -0 1
|
||||
611 1 6.910961 1 -0 1
|
||||
612 1 16.893517 1 -0 1
|
||||
613 0 -5.226982 1E-15 1.4415419267167138E-15 0
|
||||
614 0 -5.5724964 1E-15 1.4415419267167138E-15 0
|
||||
615 0 -3.9466457 1E-15 1.4415419267167138E-15 0
|
||||
616 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
617 0 ? ? ? 0
|
||||
618 0 -4.533843 1E-15 1.4415419267167138E-15 0
|
||||
619 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
620 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
621 0 0.35605335 0.6363636 1.4594315756416352 1
|
||||
622 0 -2.2074018 0.071428575 0.10691520887754996 0
|
||||
623 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
624 0 -3.8450823 1E-15 1.4415419267167138E-15 0
|
||||
625 0 -3.4678864 1E-15 1.4415419267167138E-15 0
|
||||
626 1 5.7601643 0.98 0.029146317580716615 1
|
||||
627 0 -4.1699953 1E-15 1.4415419267167138E-15 0
|
||||
628 0 -5.9689827 1E-15 1.4415419267167138E-15 0
|
||||
629 0 -5.0209913 1E-15 1.4415419267167138E-15 0
|
||||
630 0 -3.358376 1E-15 1.4415419267167138E-15 0
|
||||
631 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
632 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
633 1 4.3299503 0.9285714 0.10691524360481655 1
|
||||
634 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
635 0 -4.61419 1E-15 1.4415419267167138E-15 0
|
||||
636 1 10.127518 1 -0 1
|
||||
637 0 -2.4650178 1E-15 1.4415419267167138E-15 0
|
||||
638 0 -5.0209913 1E-15 1.4415419267167138E-15 0
|
||||
639 0 -3.93614 1E-15 1.4415419267167138E-15 0
|
||||
640 0 -4.4101357 1E-15 1.4415419267167138E-15 0
|
||||
641 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
642 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
643 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
644 0 -5.9689827 1E-15 1.4415419267167138E-15 0
|
||||
645 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
646 0 -6.021953 1E-15 1.4415419267167138E-15 0
|
||||
647 0 -5.832123 1E-15 1.4415419267167138E-15 0
|
||||
648 1 12.236198 1 -0 1
|
||||
649 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
650 0 -3.7496567 1E-15 1.4415419267167138E-15 0
|
||||
651 0 -5.217549 1E-15 1.4415419267167138E-15 0
|
||||
652 0 -3.8628197 1E-15 1.4415419267167138E-15 0
|
||||
653 0 -4.533843 1E-15 1.4415419267167138E-15 0
|
||||
654 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
655 0 -5.0078387 1E-15 1.4415419267167138E-15 0
|
||||
656 0 -4.059848 1E-15 1.4415419267167138E-15 0
|
||||
657 0 -0.4869156 0.5 1 0
|
||||
658 1 9.086258 1 -0 1
|
||||
659 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
660 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
661 0 -4.046695 1E-15 1.4415419267167138E-15 0
|
||||
662 0 -5.3686323 1E-15 1.4415419267167138E-15 0
|
||||
663 0 -5.3686323 1E-15 1.4415419267167138E-15 0
|
||||
664 0 -4.3969836 1E-15 1.4415419267167138E-15 0
|
||||
665 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
666 0 -3.4969325 1E-15 1.4415419267167138E-15 0
|
||||
667 0 -4.520691 1E-15 1.4415419267167138E-15 0
|
||||
668 1 2.8043652 0.8095238 0.30485456129516797 1
|
||||
669 1 8.147335 1 -0 1
|
||||
670 1 6.4856205 0.98 0.029146317580716615 1
|
||||
671 0 -4.087837 1E-15 1.4415419267167138E-15 0
|
||||
672 0 -4.9946866 1E-15 1.4415419267167138E-15 0
|
||||
673 0 -3.9078827 1E-15 1.4415419267167138E-15 0
|
||||
674 0 -5.95583 1E-15 1.4415419267167138E-15 0
|
||||
675 0 -4.1401944 1E-15 1.4415419267167138E-15 0
|
||||
676 0 -5.6622314 1E-15 1.4415419267167138E-15 0
|
||||
677 0 -4.5469956 1E-15 1.4415419267167138E-15 0
|
||||
678 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
679 0 -5.9689827 1E-15 1.4415419267167138E-15 0
|
||||
680 1 16.78001 1 -0 1
|
||||
681 1 9.801077 1 -0 1
|
||||
682 0 -3.3756714 1E-15 1.4415419267167138E-15 0
|
||||
683 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
684 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
685 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
686 0 -6.4429784 1E-15 1.4415419267167138E-15 0
|
||||
687 0 -4.613783 1E-15 1.4415419267167138E-15 0
|
||||
688 0 -5.0209913 1E-15 1.4415419267167138E-15 0
|
||||
689 0 -4.1777043 1E-15 1.4415419267167138E-15 0
|
||||
690 0 -5.832123 1E-15 1.4415419267167138E-15 0
|
||||
691 1 4.4967804 0.9285714 0.10691524360481655 1
|
||||
692 0 -5.494987 1E-15 1.4415419267167138E-15 0
|
||||
693 0 -4.6842284 1E-15 1.4415419267167138E-15 0
|
||||
694 0 -4.9545784 1E-15 1.4415419267167138E-15 0
|
||||
695 0 -5.9689827 1E-15 1.4415419267167138E-15 0
|
||||
696 1 6.7127857 0.98 0.029146317580716615 1
|
||||
697 1 5.064644 0.98 0.029146317580716615 1
|
||||
698 1 6.1036224 0.98 0.029146317580716615 1
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 234 | 5 | 0.9791
|
||||
negative || 12 | 432 | 0.9730
|
||||
||======================
|
||||
Precision || 0.9512 | 0.9886 |
|
||||
OVERALL 0/1 ACCURACY: 0.975110
|
||||
LOG LOSS/instance: 0.120617
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.870860
|
||||
AUC: 0.996146
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996146 (0.0000)
|
||||
Accuracy: 0.975110 (0.0000)
|
||||
Positive precision: 0.951220 (0.0000)
|
||||
Positive recall: 0.979079 (0.0000)
|
||||
Negative precision: 0.988558 (0.0000)
|
||||
Negative recall: 0.972973 (0.0000)
|
||||
Log-loss: 0.120617 (0.0000)
|
||||
Log-loss reduction: 0.870860 (0.0000)
|
||||
F1 Score: 0.964948 (0.0000)
|
||||
AUPRC: 0.992065 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.120617 0.87086 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.5726995 0.047724232 0.070548673242470078 0
|
||||
1 0 3.6101446 0.92095214 3.6611298070520353 1
|
||||
2 0 -4.0709443 0.03320216 0.048713845243635014 0
|
||||
3 0 2.470542 0.83073896 2.5626781731328263 1
|
||||
4 0 -3.4358397 0.05267027 0.078061433982946932 0
|
||||
5 1 12.382593 0.99988943 0.0001595227277818751 1
|
||||
6 0 -1.4209604 0.20405398 0.32925750806385767 0
|
||||
7 0 -4.7010875 0.02084881 0.030396453391412736 0
|
||||
8 0 -4.6745405 0.021263903 0.031008185910590607 0
|
||||
9 0 -4.406417 0.025935683 0.037911059564125327 0
|
||||
10 0 -5.5593443 0.010982092 0.01593145032913975 0
|
||||
11 0 -5.4818344 0.011639431 0.016890640083211839 0
|
||||
12 1 -0.14206886 0.40347567 1.3094464075888503 0
|
||||
13 0 -4.5691886 0.022992346 0.033558231039772665 0
|
||||
14 1 9.321613 0.998874 0.0016253773809814112 1
|
||||
15 1 1.3856993 0.6830734 0.54988747944718552 1
|
||||
16 0 -4.533843 0.023602419 0.034459373415090033 0
|
||||
17 0 -4.046695 0.03379773 0.049602852281362811 0
|
||||
18 1 7.8903713 0.9966727 0.0048082975514731332 1
|
||||
19 0 -3.0987039 0.06699134 0.10003761775121778 0
|
||||
20 1 7.528511 0.9956263 0.0063237968594455725 1
|
||||
21 1 7.875206 0.9966343 0.0048638619113426269 1
|
||||
22 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.4686823 0.011754767 0.017059004011272673 0
|
||||
25 1 1.741828 0.73848027 0.43736871808798289 1
|
||||
26 0 -4.9710746 0.01705355 0.024815273012732428 0
|
||||
27 0 -4.059848 0.03347343 0.049118699463849731 0
|
||||
28 0 -5.4818344 0.011639431 0.016890640083211839 0
|
||||
29 0 -5.8557806 0.008789949 0.012737278390963667 0
|
||||
30 0 -5.0985007 0.015506633 0.022546608302385175 0
|
||||
31 0 -4.9946866 0.016755885 0.024378447925635292 0
|
||||
32 1 7.46414 0.9954084 0.0066395103905325147 1
|
||||
33 0 -4.6892586 0.02103278 0.030667542438444956 0
|
||||
34 0 -4.71424 0.020646105 0.030097814327393193 0
|
||||
35 0 -5.4818344 0.011639431 0.016890640083211839 0
|
||||
36 1 9.09911 0.99866724 0.0019240484689165348 1
|
||||
37 0 -1.113348 0.24456774 0.40462569514389818 0
|
||||
38 1 6.140955 0.9875705 0.018044317542393044 1
|
||||
39 1 2.5109034 0.83500063 0.26015080155533699 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 3.3300762 0.90403634 0.14554732340521623 1
|
||||
42 1 8.577511 0.99802166 0.0028569650360322531 1
|
||||
43 1 0.49126053 0.5223421 0.93693314652059867 1
|
||||
44 1 8.255751 0.9974761 0.0036458194620463992 1
|
||||
45 0 -5.63222 0.010397597 0.015079091662558042 0
|
||||
46 1 4.5673847 0.96013206 0.058695238307073898 1
|
||||
47 0 -5.95583 0.008152755 0.011810147478593156 0
|
||||
48 0 -3.4358397 0.05267027 0.078061433982946932 0
|
||||
49 1 5.3666534 0.9778563 0.032305655935016012 1
|
||||
50 1 2.5949688 0.8436 0.24536904232329945 1
|
||||
51 1 0.12595844 0.4532164 1.1417280158533796 1
|
||||
52 1 5.2992115 0.976721 0.033981595778575298 1
|
||||
53 1 8.407227 0.99774945 0.0032505195711204126 1
|
||||
54 1 7.649309 0.9960077 0.0057712269479890379 1
|
||||
55 1 4.478711 0.957476 0.062691739926295564 1
|
||||
56 1 5.5541334 0.9807353 0.02806428572372743 1
|
||||
57 1 1.6657066 0.7271758 0.45962396180677062 1
|
||||
58 1 2.5265894 0.8366335 0.25733232225246744 1
|
||||
59 1 1.7368536 0.7377509 0.43879434296981179 1
|
||||
60 1 2.3288136 0.8150797 0.29498697868205404 1
|
||||
61 0 -5.5060835 0.0114297075 0.016584542177967555 0
|
||||
62 1 6.380089 0.98961115 0.015066341261387473 1
|
||||
63 1 0.33490086 0.49270463 1.0212050663068744 1
|
||||
64 0 -5.95583 0.008152755 0.011810147478593156 0
|
||||
65 1 3.8072634 0.9311753 0.1028753176207461 1
|
||||
66 0 -4.046695 0.03379773 0.049602852281362811 0
|
||||
67 1 4.218014 0.9486548 0.076044929659653121 1
|
||||
68 1 10.826725 0.9996402 0.00051913703181331893 1
|
||||
69 0 -5.271654 0.013623926 0.019790289199966723 0
|
||||
70 0 -3.4726496 0.051294282 0.075967452463448076 0
|
||||
71 1 7.895046 0.99668443 0.0047913008067115544 1
|
||||
72 0 -2.1755848 0.1263537 0.1948787725155719 0
|
||||
73 1 8.9055195 0.9984568 0.0022281210940979582 1
|
||||
74 1 2.5993576 0.8440387 0.2446189028997863 1
|
||||
75 0 -4.0411606 0.033935104 0.049807988000729261 0
|
||||
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586 1 13.98102 0.9999671 4.7467992532759307E-05 1
|
||||
587 0 -3.8628197 0.03866103 0.056882875876087251 0
|
||||
588 1 5.463169 0.9793874 0.030048455526317758 1
|
||||
589 0 -4.5469956 0.023373578 0.034121284818622755 0
|
||||
590 1 3.9684029 0.9386082 0.09140498577121392 1
|
||||
591 1 5.966527 0.98583704 0.020578904318245012 1
|
||||
592 1 5.7801704 0.98372144 0.023678256090487241 1
|
||||
593 0 -4.253397 0.02903525 0.042509173626544897 0
|
||||
594 1 5.101775 0.97306114 0.039397632242971693 1
|
||||
595 0 -4.059848 0.03347343 0.049118699463849731 0
|
||||
596 0 -4.2402444 0.029317858 0.04292914403934768 0
|
||||
597 0 -2.9855018 0.07256243 0.10867791847551245 0
|
||||
598 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
599 0 -3.6294346 0.04580593 0.067645376058973333 0
|
||||
600 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
601 0 -6.046492 0.0076150266 0.011028204574905828 0
|
||||
602 0 -4.533843 0.023602419 0.034459373415090033 0
|
||||
603 1 4.8058815 0.9665091 0.049144773305248697 1
|
||||
604 1 6.1928043 0.9880442 0.01735250950197232 1
|
||||
605 1 9.955452 0.9993035 0.0010051587149210726 1
|
||||
606 0 -4.715564 0.020625811 0.030067920071712086 0
|
||||
607 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
608 1 11.148428 0.9997181 0.00040671030044872971 1
|
||||
609 0 -4.5469956 0.023373578 0.034121284818622755 0
|
||||
610 1 8.926189 0.99848074 0.0021934995672910755 1
|
||||
611 1 6.910961 0.993031 0.010089302795775161 1
|
||||
612 1 16.893517 0.99999636 5.2454803872987944E-06 1
|
||||
613 0 -5.226982 0.014086901 0.020467605961895518 0
|
||||
614 0 -5.5724964 0.010874255 0.015774155610380477 0
|
||||
615 0 -3.9466457 0.03636577 0.053442453015430637 0
|
||||
616 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
617 0 ? ? ? 0
|
||||
618 0 -4.533843 0.023602419 0.034459373415090033 0
|
||||
619 0 -4.059848 0.03347343 0.049118699463849731 0
|
||||
620 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
621 0 0.35605335 0.4967156 0.99055422977402885 1
|
||||
622 0 -2.2074018 0.12371333 0.19052518105806376 0
|
||||
623 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
624 0 -3.8450823 0.039164227 0.057638228932157462 0
|
||||
625 0 -3.4678864 0.0514704 0.076235297856163745 0
|
||||
626 1 5.7601643 0.98347664 0.024037311317372478 1
|
||||
627 0 -4.1699953 0.03087303 0.045242403057694114 0
|
||||
628 0 -5.9689827 0.00807247 0.011693373854840414 0
|
||||
629 0 -5.0209913 0.016430287 0.023900783632758753 0
|
||||
630 0 -3.358376 0.05568055 0.082653110243383579 0
|
||||
631 0 -4.059848 0.03347343 0.049118699463849731 0
|
||||
632 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
633 1 4.3299503 0.95263666 0.070002027657626642 1
|
||||
634 0 -5.494987 0.01152521 0.016723922858878242 0
|
||||
635 0 -4.61419 0.022237854 0.032444542423638763 0
|
||||
636 1 10.127518 0.9993887 0.00088219670683892097 1
|
||||
637 0 -2.4650178 0.10403733 0.1584894700655759 0
|
||||
638 0 -5.0209913 0.016430287 0.023900783632758753 0
|
||||
639 0 -3.93614 0.036646076 0.053862170913072119 0
|
||||
640 0 -4.4101357 0.02586451 0.037805647681891714 0
|
||||
641 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
642 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
643 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
644 0 -5.9689827 0.00807247 0.011693373854840414 0
|
||||
645 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
646 0 -6.021953 0.0077569964 0.011234610256342446 0
|
||||
647 0 -5.832123 0.008947697 0.012966896248755017 0
|
||||
648 1 12.236198 0.9998765 0.00017818502951934642 1
|
||||
649 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
650 0 -3.7496567 0.041980952 0.061873754012903771 0
|
||||
651 0 -5.217549 0.014186632 0.020613550094586007 0
|
||||
652 0 -3.8628197 0.03866103 0.056882875876087251 0
|
||||
653 0 -4.533843 0.023602419 0.034459373415090033 0
|
||||
654 0 -4.520691 0.023833437 0.034800759324198648 0
|
||||
655 0 -5.0078387 0.016592303 0.024138448331127087 0
|
||||
656 0 -4.059848 0.03347343 0.049118699463849731 0
|
||||
657 0 -0.4869156 0.3424043 0.6047272426002388 0
|
||||
658 1 9.086258 0.9986542 0.0019429058245893991 1
|
||||
659 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
660 0 -5.95583 0.008152755 0.011810147478593156 0
|
||||
661 0 -4.046695 0.03379773 0.049602852281362811 0
|
||||
662 0 -5.3686323 0.012669891 0.018395571683516153 0
|
||||
663 0 -5.3686323 0.012669891 0.018395571683516153 0
|
||||
664 0 -4.3969836 0.026117077 0.038179748349732472 0
|
||||
665 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
666 0 -3.4969325 0.050405253 0.074616140216202456 0
|
||||
667 0 -4.520691 0.023833437 0.034800759324198648 0
|
||||
668 1 2.8043652 0.86343354 0.21184295985895515 1
|
||||
669 1 8.147335 0.99726033 0.0039579294672795707 1
|
||||
670 1 6.4856205 0.99040276 0.013912762642680866 1
|
||||
671 0 -4.087837 0.032793265 0.048103804028856063 0
|
||||
672 0 -4.9946866 0.016755885 0.024378447925635292 0
|
||||
673 0 -3.9078827 0.03741038 0.055007226361106963 0
|
||||
674 0 -5.95583 0.008152755 0.011810147478593156 0
|
||||
675 0 -4.1401944 0.031556632 0.046260409123226896 0
|
||||
676 0 -5.6622314 0.010165936 0.014741403595886524 0
|
||||
677 0 -4.5469956 0.023373578 0.034121284818622755 0
|
||||
678 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
679 0 -5.9689827 0.00807247 0.011693373854840414 0
|
||||
680 1 16.78001 0.99999607 5.6754386418026423E-06 1
|
||||
681 1 9.801077 0.9992171 0.0011299384356403619 1
|
||||
682 0 -3.3756714 0.05499471 0.08160568921637594 0
|
||||
683 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
684 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
685 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
686 0 -6.4429784 0.005648197 0.0081717255492616877 0
|
||||
687 0 -4.613783 0.022244573 0.032454455742594257 0
|
||||
688 0 -5.0209913 0.016430287 0.023900783632758753 0
|
||||
689 0 -4.1777043 0.03069854 0.044982670054638463 0
|
||||
690 0 -5.832123 0.008947697 0.012966896248755017 0
|
||||
691 1 4.4967804 0.95803064 0.061856295892801602 1
|
||||
692 0 -5.494987 0.01152521 0.016723922858878242 0
|
||||
693 0 -4.6842284 0.021111494 0.030783546498899184 0
|
||||
694 0 -4.9545784 0.017264586 0.02512504882755813 0
|
||||
695 0 -5.9689827 0.00807247 0.011693373854840414 0
|
||||
696 1 6.7127857 0.9919096 0.011719418003861082 1
|
||||
697 1 5.064644 0.97231287 0.040507480679364792 1
|
||||
698 1 6.1036224 0.9872181 0.018559275661035816 1
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)
|
||||
Not training a calibrator because a valid calibrator trainer was not provided.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 234 | 5 | 0.9791
|
||||
negative || 12 | 432 | 0.9730
|
||||
||======================
|
||||
Precision || 0.9512 | 0.9886 |
|
||||
OVERALL 0/1 ACCURACY: 0.975110
|
||||
LOG LOSS/instance: NaN
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.000000
|
||||
AUC: 0.996146
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996146 (0.0000)
|
||||
Accuracy: 0.975110 (0.0000)
|
||||
Positive precision: 0.951220 (0.0000)
|
||||
Positive recall: 0.979079 (0.0000)
|
||||
Negative precision: 0.988558 (0.0000)
|
||||
Negative recall: 0.972973 (0.0000)
|
||||
Log-loss: NaN (0.0000)
|
||||
Log-loss reduction: 0.000000 (0.0000)
|
||||
F1 Score: 0.964948 (0.0000)
|
||||
AUPRC: 0.992065 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Warning: Data does not contain a probability column. Will not output the Log-loss column
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
AveragedPerceptron
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 NaN 0 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Assigned
|
||||
0 0 -3.5726995 0
|
||||
1 0 3.6101446 1
|
||||
2 0 -4.0709443 0
|
||||
3 0 2.470542 1
|
||||
4 0 -3.4358397 0
|
||||
5 1 12.382593 1
|
||||
6 0 -1.4209604 0
|
||||
7 0 -4.7010875 0
|
||||
8 0 -4.6745405 0
|
||||
9 0 -4.406417 0
|
||||
10 0 -5.5593443 0
|
||||
11 0 -5.4818344 0
|
||||
12 1 -0.14206886 0
|
||||
13 0 -4.5691886 0
|
||||
14 1 9.321613 1
|
||||
15 1 1.3856993 1
|
||||
16 0 -4.533843 0
|
||||
17 0 -4.046695 0
|
||||
18 1 7.8903713 1
|
||||
19 0 -3.0987039 0
|
||||
20 1 7.528511 1
|
||||
21 1 7.875206 1
|
||||
22 0 -5.0078387 0
|
||||
23 1 ? 0
|
||||
24 0 -5.4686823 0
|
||||
25 1 1.741828 1
|
||||
26 0 -4.9710746 0
|
||||
27 0 -4.059848 0
|
||||
28 0 -5.4818344 0
|
||||
29 0 -5.8557806 0
|
||||
30 0 -5.0985007 0
|
||||
31 0 -4.9946866 0
|
||||
32 1 7.46414 1
|
||||
33 0 -4.6892586 0
|
||||
34 0 -4.71424 0
|
||||
35 0 -5.4818344 0
|
||||
36 1 9.09911 1
|
||||
37 0 -1.113348 0
|
||||
38 1 6.140955 1
|
||||
39 1 2.5109034 1
|
||||
40 0 ? 0
|
||||
41 1 3.3300762 1
|
||||
42 1 8.577511 1
|
||||
43 1 0.49126053 1
|
||||
44 1 8.255751 1
|
||||
45 0 -5.63222 0
|
||||
46 1 4.5673847 1
|
||||
47 0 -5.95583 0
|
||||
48 0 -3.4358397 0
|
||||
49 1 5.3666534 1
|
||||
50 1 2.5949688 1
|
||||
51 1 0.12595844 1
|
||||
52 1 5.2992115 1
|
||||
53 1 8.407227 1
|
||||
54 1 7.649309 1
|
||||
55 1 4.478711 1
|
||||
56 1 5.5541334 1
|
||||
57 1 1.6657066 1
|
||||
58 1 2.5265894 1
|
||||
59 1 1.7368536 1
|
||||
60 1 2.3288136 1
|
||||
61 0 -5.5060835 0
|
||||
62 1 6.380089 1
|
||||
63 1 0.33490086 1
|
||||
64 0 -5.95583 0
|
||||
65 1 3.8072634 1
|
||||
66 0 -4.046695 0
|
||||
67 1 4.218014 1
|
||||
68 1 10.826725 1
|
||||
69 0 -5.271654 0
|
||||
70 0 -3.4726496 0
|
||||
71 1 7.895046 1
|
||||
72 0 -2.1755848 0
|
||||
73 1 8.9055195 1
|
||||
74 1 2.5993576 1
|
||||
75 0 -4.0411606 0
|
||||
76 0 -5.0750337 0
|
||||
77 0 -3.499567 0
|
||||
78 0 -3.6211972 0
|
||||
79 0 -5.391173 0
|
||||
80 0 -2.7157316 0
|
||||
81 0 -4.2284155 0
|
||||
82 0 -3.4452734 0
|
||||
83 0 -2.1223526 0
|
||||
84 1 9.694054 1
|
||||
85 1 6.2895603 1
|
||||
86 1 2.6168842 1
|
||||
87 1 6.919142 1
|
||||
88 0 -4.046695 0
|
||||
89 0 -5.085745 0
|
||||
90 0 -5.4686823 0
|
||||
91 0 -5.189559 0
|
||||
92 0 -4.046695 0
|
||||
93 0 -5.95583 0
|
||||
94 0 -4.9946866 0
|
||||
95 0 -5.4686823 0
|
||||
96 0 -5.663555 0
|
||||
97 0 -3.5726995 0
|
||||
98 1 8.590233 1
|
||||
99 1 10.917194 1
|
||||
100 1 4.8476696 1
|
||||
101 1 -0.84280396 0
|
||||
102 0 -3.7530966 0
|
||||
103 1 1.7746439 1
|
||||
104 1 12.140858 1
|
||||
105 1 2.5560713 1
|
||||
106 1 9.259369 1
|
||||
107 1 6.720646 1
|
||||
108 0 -5.5617743 0
|
||||
109 1 6.871727 1
|
||||
110 0 -2.766693 0
|
||||
111 1 3.848031 1
|
||||
112 1 9.425768 1
|
||||
113 1 9.506624 1
|
||||
114 0 -3.0727458 0
|
||||
115 0 -4.6439905 0
|
||||
116 0 -0.66188717 0
|
||||
117 1 9.617275 1
|
||||
118 0 -5.3621607 0
|
||||
119 0 -3.9435177 0
|
||||
120 0 -4.8696556 0
|
||||
121 0 -3.469522 0
|
||||
122 1 9.680523 1
|
||||
123 1 3.8165932 1
|
||||
124 1 7.6522446 1
|
||||
125 0 -5.95583 0
|
||||
126 1 8.564951 1
|
||||
127 0 -4.520691 0
|
||||
128 1 4.84898 1
|
||||
129 0 -5.717684 0
|
||||
130 0 -3.4726496 0
|
||||
131 0 -4.9946866 0
|
||||
132 1 8.602232 1
|
||||
133 0 -4.810811 0
|
||||
134 0 -4.9171767 0
|
||||
135 0 -2.7288966 0
|
||||
136 0 -4.533843 0
|
||||
137 0 -5.494987 0
|
||||
138 0 -4.2402444 0
|
||||
139 0 ? 0
|
||||
140 0 -5.494987 0
|
||||
141 0 -5.9689827 0
|
||||
142 1 4.4324036 1
|
||||
143 0 -4.6439905 0
|
||||
144 0 -5.4818344 0
|
||||
145 0 ? 0
|
||||
146 1 1.3394346 1
|
||||
147 0 -5.4154215 0
|
||||
148 0 -1.0123739 0
|
||||
149 1 11.461615 1
|
||||
150 0 -5.5593443 0
|
||||
151 1 5.006485 1
|
||||
152 1 9.715748 1
|
||||
153 0 -4.121497 0
|
||||
154 0 -6.4429784 0
|
||||
155 1 3.7769232 1
|
||||
156 0 -5.5348053 0
|
||||
157 0 -4.9946866 0
|
||||
158 0 ? 0
|
||||
159 1 12.346203 1
|
||||
160 1 9.039494 1
|
||||
161 0 -3.8496675 0
|
||||
162 0 -4.520691 0
|
||||
163 0 -3.3870554 0
|
||||
164 0 ? 0
|
||||
165 0 -3.3999205 0
|
||||
166 1 7.976185 1
|
||||
167 1 8.355644 1
|
||||
168 0 -4.520691 0
|
||||
169 0 -6.2282124 0
|
||||
170 0 -5.494987 0
|
||||
171 0 -5.4686823 0
|
||||
172 0 -5.95583 0
|
||||
173 1 15.1560135 1
|
||||
174 1 6.1769257 1
|
||||
175 1 7.842922 1
|
||||
176 0 -4.9946866 0
|
||||
177 1 4.766121 1
|
||||
178 0 -4.046695 0
|
||||
179 1 2.290575 1
|
||||
180 0 -5.5593443 0
|
||||
181 0 -6.4429784 0
|
||||
182 0 -3.0987039 0
|
||||
183 1 9.1599655 1
|
||||
184 1 6.2014637 1
|
||||
185 0 -5.0853486 0
|
||||
186 1 5.7654095 1
|
||||
187 1 13.977451 1
|
||||
188 1 9.065283 1
|
||||
189 0 -4.7540584 0
|
||||
190 1 11.957216 1
|
||||
191 1 10.956871 1
|
||||
192 0 -4.059848 0
|
||||
193 0 -5.4686823 0
|
||||
194 0 -4.520691 0
|
||||
195 0 -4.046695 0
|
||||
196 0 6.8652763 1
|
||||
197 0 -2.6564164 0
|
||||
198 0 -6.4429784 0
|
||||
199 0 -5.0078387 0
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669 1 8.147335 1
|
||||
670 1 6.4856205 1
|
||||
671 0 -4.087837 0
|
||||
672 0 -4.9946866 0
|
||||
673 0 -3.9078827 0
|
||||
674 0 -5.95583 0
|
||||
675 0 -4.1401944 0
|
||||
676 0 -5.6622314 0
|
||||
677 0 -4.5469956 0
|
||||
678 0 -6.4429784 0
|
||||
679 0 -5.9689827 0
|
||||
680 1 16.78001 1
|
||||
681 1 9.801077 1
|
||||
682 0 -3.3756714 0
|
||||
683 0 -6.4429784 0
|
||||
684 0 -6.4429784 0
|
||||
685 0 -6.4429784 0
|
||||
686 0 -6.4429784 0
|
||||
687 0 -4.613783 0
|
||||
688 0 -5.0209913 0
|
||||
689 0 -4.1777043 0
|
||||
690 0 -5.832123 0
|
||||
691 1 4.4967804 1
|
||||
692 0 -5.494987 0
|
||||
693 0 -4.6842284 0
|
||||
694 0 -4.9545784 0
|
||||
695 0 -5.9689827 0
|
||||
696 1 6.7127857 1
|
||||
697 1 5.064644 1
|
||||
698 1 6.1036224 1
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -1.5274918 0.017604085 0.025623532721295594 0
|
||||
1 0 1.4144671 0.9358385 3.9621487810879379 1
|
||||
2 0 -1.6407909 0.01365415 0.019834496464596688 0
|
||||
3 0 1.2451689 0.90840983 3.4486634832869028 1
|
||||
4 0 -1.52967 0.017518476 0.025497817268550486 0
|
||||
5 1 4.3474255 0.99991405 0.00012400482044924764 1
|
||||
6 0 -0.47047758 0.16604209 0.26195352151813267 0
|
||||
7 0 -1.853452 0.008455773 0.012250970625256392 0
|
||||
8 0 -1.7931806 0.009688178 0.014045233221232831 0
|
||||
9 0 -1.677077 0.012584503 0.01827080696298632 0
|
||||
10 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
11 0 -2.064441 0.0052458635 0.0075881017189601109 0
|
||||
12 1 -0.22798824 0.25701508 1.9600750890275802 0
|
||||
13 0 -1.7540903 0.010580935 0.015346395394965805 0
|
||||
14 1 3.16408 0.9987278 0.0018365673397693367 1
|
||||
15 1 0.3537979 0.56556815 0.82222722022105776 1
|
||||
16 0 -1.7677684 0.010259655 0.014878006617854059 0
|
||||
17 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
18 1 2.7824595 0.99697065 0.0043770562861335516 1
|
||||
19 0 -1.3791554 0.024507888 0.035797887389887428 0
|
||||
20 1 2.2761443 0.99046284 0.01382524609823586 1
|
||||
21 1 2.7155144 0.9964734 0.0050968413754289565 1
|
||||
22 0 -1.9161046 0.007339345 0.010627483647687244 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
25 1 0.5794046 0.68519133 0.5454211914119842 1
|
||||
26 0 -1.8810809 0.007944069 0.011506634268045332 0
|
||||
27 0 -1.619432 0.01432519 0.020816338614916444 0
|
||||
28 0 -2.064441 0.0052458635 0.0075881017189601109 0
|
||||
29 0 -2.06662 0.005220022 0.0075506243315169768 0
|
||||
30 0 -1.9717722 0.006470872 0.0093658304055144765 0
|
||||
31 0 -1.9725006 0.0064602136 0.0093503533422127551 0
|
||||
32 1 2.5108254 0.9943901 0.0081162045718068156 1
|
||||
33 0 -1.8994217 0.0076214722 0.011037575117714665 0
|
||||
34 0 -1.797056 0.009603843 0.013922378499413527 0
|
||||
35 0 -2.064441 0.0052458635 0.0075881017189601109 0
|
||||
36 1 3.0295837 0.9982725 0.0024944418066934052 1
|
||||
37 0 -0.9763706 0.059167836 0.087990712593621456 0
|
||||
38 1 1.9792311 0.9814142 0.027065950749429996 1
|
||||
39 1 0.8320954 0.79468054 0.33155308613045453 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 0.88030076 0.81201965 0.30041346219868831 1
|
||||
42 1 3.2987616 0.9990636 0.0013515566126819322 1
|
||||
43 1 0.21289468 0.485706 1.0418447820693235 1
|
||||
44 1 2.9403288 0.9978838 0.0030562710053100568 1
|
||||
45 0 -2.121977 0.0046044113 0.0066581014742497615 0
|
||||
46 1 1.9658577 0.9808503 0.02789516002406054 1
|
||||
47 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
48 0 -1.52967 0.017518476 0.025497817268550486 0
|
||||
49 1 2.3192227 0.9913466 0.012538549766383886 1
|
||||
50 1 0.899966 0.8187624 0.28848320576972181 1
|
||||
51 1 -0.063476086 0.33475077 1.5788407142580643 0
|
||||
52 1 1.8613322 0.97582626 0.035303782347898022 1
|
||||
53 1 2.6479886 0.99588937 0.0059426141002280079 1
|
||||
54 1 2.4039233 0.99285424 0.010346165844936006 1
|
||||
55 1 1.712507 0.9663979 0.049310802829332187 1
|
||||
56 1 2.5115206 0.9943989 0.0081034061136448163 1
|
||||
57 1 0.26381683 0.51469857 0.95820033461072918 1
|
||||
58 1 0.8582065 0.8042159 0.31434522077516569 1
|
||||
59 1 0.34514594 0.56071943 0.83464903092710485 1
|
||||
60 1 0.76229215 0.76751965 0.38172440278002573 1
|
||||
61 0 -2.029404 0.0056792544 0.0082167869478707006 0
|
||||
62 1 2.865918 0.99749386 0.0036201294361900668 1
|
||||
63 1 0.24355912 0.5031658 0.99089428185497785 1
|
||||
64 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
65 1 0.9369471 0.8309296 0.26720188349008961 1
|
||||
66 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
67 1 1.821151 0.97357035 0.038642868012306673 1
|
||||
68 1 3.485832 0.99938834 0.00088271297047849088 1
|
||||
69 0 -2.0291107 0.005683028 0.0082222623803676655 0
|
||||
70 0 -1.3813345 0.024389489 0.035622792851633903 0
|
||||
71 1 2.5234272 0.99454796 0.0078871466346560996 1
|
||||
72 0 -1.1687906 0.03898821 0.057373964727207627 0
|
||||
73 1 3.0788867 0.9984557 0.0022296713311501402 1
|
||||
74 1 0.9039061 0.82009053 0.28614491284583687 1
|
||||
75 0 -1.6347486 0.013840776 0.020107493699165757 0
|
||||
76 0 -1.8556311 0.008414254 0.012190561396366968 0
|
||||
77 0 -1.3649704 0.02529241 0.036958615643993616 0
|
||||
78 0 -1.4574177 0.020588245 0.030012583721117519 0
|
||||
79 0 -2.0087733 0.005950909 0.0086109941684838071 0
|
||||
80 0 -1.4506671 0.020900631 0.03047280868260643 0
|
||||
81 0 -1.6946893 0.012095576 0.017556621812877982 0
|
||||
82 0 -1.4368005 0.021556938 0.031440195652101426 0
|
||||
83 0 -1.2730298 0.031002669 0.045435402490986702 0
|
||||
84 1 3.3035352 0.99907374 0.0013369244601044573 1
|
||||
85 1 2.6141112 0.995561 0.0064183741024694194 1
|
||||
86 1 0.9894326 0.8470688 0.23944896584029779 1
|
||||
87 1 2.5067976 0.99433863 0.0081908356804006321 1
|
||||
88 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
89 0 -1.9356945 0.0070212646 0.010165272115948743 0
|
||||
90 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
91 0 -1.9349658 0.007032846 0.010182098876140151 0
|
||||
92 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
93 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
94 0 -1.9725006 0.0064602136 0.0093503533422127551 0
|
||||
95 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
96 0 -2.0833023 0.0050263484 0.0072697734628044833 0
|
||||
97 0 -1.5274918 0.017604085 0.025623532721295594 0
|
||||
98 1 3.3660557 0.9991966 0.0011595429326632949 1
|
||||
99 1 3.576234 0.9995021 0.00071846440984066307 1
|
||||
100 1 1.9669368 0.98089635 0.027827392562706721 1
|
||||
101 1 -0.20627713 0.26657256 1.9073997863491894 0
|
||||
102 0 -1.5567794 0.016486624 0.023983421652861426 0
|
||||
103 1 0.20348358 0.48035252 1.05783453582061 1
|
||||
104 1 3.9422872 0.9997837 0.00031209627523989933 1
|
||||
105 1 0.5858877 0.68836826 0.53874751545645705 1
|
||||
106 1 3.3557017 0.99917746 0.0011871686071808722 1
|
||||
107 1 2.5422742 0.9947759 0.0075565510615385034 1
|
||||
108 0 -2.0293248 0.0056802724 0.008218263907255172 0
|
||||
109 1 2.2740915 0.99041855 0.013889754306585319 1
|
||||
110 0 -1.224869 0.03447371 0.050612552149072614 0
|
||||
111 1 1.5082662 0.94753504 0.077748802573421025 1
|
||||
112 1 2.6745536 0.9961298 0.0055943353159972516 1
|
||||
113 1 3.4366615 0.99931586 0.00098734621438804427 1
|
||||
114 0 -1.1569865 0.04000832 0.058906194575417792 0
|
||||
115 0 -1.5712247 0.015961448 0.0232132568855868 0
|
||||
116 0 -0.38790345 0.19374856 0.31069826905902698 0
|
||||
117 1 2.9528043 0.997943 0.0029707030722609194 1
|
||||
118 0 -1.8266358 0.00898364 0.013019220830063053 0
|
||||
119 0 -1.5500998 0.016735177 0.024348065227316973 0
|
||||
120 0 -1.9287093 0.007133081 0.010327738961349225 0
|
||||
121 0 -1.4017634 0.023306306 0.034021913539006958 0
|
||||
122 1 3.8195612 0.9997139 0.00041281741889252245 1
|
||||
123 1 1.4817827 0.9444536 0.082448179081865927 1
|
||||
124 1 2.5534494 0.99490654 0.0073670806393172483 1
|
||||
125 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
126 1 2.6680787 0.99607253 0.0056772964309789609 1
|
||||
127 0 -1.8241644 0.009033903 0.013092393555207428 0
|
||||
128 1 1.9772441 0.9813314 0.027187659798911711 1
|
||||
129 0 -1.8593771 0.008343352 0.01208740772320846 0
|
||||
130 0 -1.3813345 0.024389489 0.035622792851633903 0
|
||||
131 0 -1.9725006 0.0064602136 0.0093503533422127551 0
|
||||
132 1 3.4043772 0.9992637 0.0010626421007376144 1
|
||||
133 0 -1.8243784 0.00902954 0.013086042705976558 0
|
||||
134 0 -1.8604369 0.008323401 0.01205838293979709 0
|
||||
135 0 -1.160666 0.039687626 0.058424327569763942 0
|
||||
136 0 -1.7677684 0.010259655 0.014878006617854059 0
|
||||
137 0 -2.008045 0.0059607327 0.0086252515280869323 0
|
||||
138 0 -1.6487195 0.013413025 0.019481854101213815 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -2.008045 0.0059607327 0.0086252515280869323 0
|
||||
141 0 -2.1563811 0.004258803 0.0061572747374996819 0
|
||||
142 1 1.4258711 0.93738085 0.093292772480968233 1
|
||||
143 0 -1.5712247 0.015961448 0.0232132568855868 0
|
||||
144 0 -2.064441 0.0052458635 0.0075881017189601109 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 0.5954063 0.69300073 0.52907121478369279 1
|
||||
147 0 -1.9737365 0.006442167 0.0093241483999488353 0
|
||||
148 0 -0.51803684 0.15157725 0.2371447869957021 0
|
||||
149 1 4.1362305 0.99986094 0.00020063171224062208 1
|
||||
150 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
151 1 1.7307074 0.96771854 0.04734059041511169 1
|
||||
152 1 3.3592126 0.99918395 0.0011777878671093308 1
|
||||
153 0 -1.4929621 0.019017342 0.027700463160706314 0
|
||||
154 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
155 1 1.3828895 0.93138134 0.10255610905824641 1
|
||||
156 0 -2.0030372 0.006028709 0.0087239120462013157 0
|
||||
157 0 -1.9725006 0.0064602136 0.0093503533422127551 0
|
||||
158 0 ? ? ? 0
|
||||
159 1 4.5293074 0.9999432 8.1952060728897203E-05 1
|
||||
160 1 3.334266 0.9991363 0.0012465526889135054 1
|
||||
161 0 -1.5841018 0.015507181 0.022547412156594421 0
|
||||
162 0 -1.8241644 0.009033903 0.013092393555207428 0
|
||||
163 0 -1.3757586 0.024693564 0.036072517211212594 0
|
||||
164 0 ? ? ? 0
|
||||
165 0 -1.4007286 0.023360029 0.034101270238708251 0
|
||||
166 1 3.1433108 0.9986662 0.0019255122730899305 1
|
||||
167 1 2.6180494 0.99560046 0.0063611951559849299 1
|
||||
168 0 -1.8241644 0.009033903 0.013092393555207428 0
|
||||
169 0 -2.287306 0.0031639924 0.0045719127530314662 0
|
||||
170 0 -2.008045 0.0059607327 0.0086252515280869323 0
|
||||
171 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
172 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
173 1 5.1644983 0.99998665 1.926218548588174E-05 1
|
||||
174 1 2.0482528 0.9840753 0.023159370685856349 1
|
||||
175 1 3.0900505 0.99849445 0.0021736916047153973 1
|
||||
176 0 -1.9725006 0.0064602136 0.0093503533422127551 0
|
||||
177 1 2.031348 0.9834604 0.024061094122559881 1
|
||||
178 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
179 1 0.9111755 0.82252085 0.28187584117752651 1
|
||||
180 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
181 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
182 0 -1.3791554 0.024507888 0.035797887389887428 0
|
||||
183 1 3.2794478 0.99902153 0.0014123246695702737 1
|
||||
184 1 2.22172 0.9892176 0.015640216828503601 1
|
||||
185 0 -2.0281682 0.0056951726 0.0082398833604486969 0
|
||||
186 1 2.0845768 0.9853214 0.021333700032620535 1
|
||||
187 1 4.438258 0.9999301 0.00010087135104534345 1
|
||||
188 1 2.9499977 0.9979298 0.002989746453144395 1
|
||||
189 0 -1.7920482 0.009712959 0.014081334315920882 0
|
||||
190 1 4.3129826 0.999907 0.00013415270474661438 1
|
||||
191 1 4.055872 0.999833 0.00024096781670331766 1
|
||||
192 0 -1.619432 0.01432519 0.020816338614916444 0
|
||||
193 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
194 0 -1.8241644 0.009033903 0.013092393555207428 0
|
||||
195 0 -1.675828 0.01261991 0.018322540453649942 0
|
||||
196 0 2.151273 0.9873643 6.3063497031242317 1
|
||||
197 0 -1.3082547 0.028680656 0.041982401601898908 0
|
||||
198 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
199 0 -1.9161046 0.007339345 0.010627483647687244 0
|
||||
200 1 3.6254156 0.9995549 0.00064232618745076891 1
|
||||
201 1 3.4570076 0.99934685 0.0009426008063710327 1
|
||||
202 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
203 0 -1.5274918 0.017604085 0.025623532721295594 0
|
||||
204 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
205 1 4.3743267 0.9999191 0.0001166949480221247 1
|
||||
206 1 2.507694 0.99435014 0.008174144958268334 1
|
||||
207 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
208 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
209 0 -1.5831592 0.015539997 0.022595502287138552 0
|
||||
210 1 5.0194874 0.9999814 2.6829543005170701E-05 1
|
||||
211 1 3.3916986 0.9992421 0.0010937942338244272 1
|
||||
212 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
213 1 5.1539555 0.9999863 1.9778140419471667E-05 1
|
||||
214 1 4.5678005 0.99994797 7.5072380308763176E-05 1
|
||||
215 1 2.7341921 0.99661976 0.0048849148054705294 1
|
||||
216 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
217 0 -2.120837 0.0046163304 0.0066753766972919753 0
|
||||
218 1 2.73866 0.9966539 0.0048354755035983493 1
|
||||
219 0 -1.141058 0.041425474 0.061037493846160626 0
|
||||
220 0 -2.0477579 0.0054479614 0.0078812347580574371 0
|
||||
221 1 3.6438277 0.9995731 0.00061600136403503912 1
|
||||
222 1 -0.94378495 0.06343784 3.9785124986557925 0
|
||||
223 1 1.8555677 0.97551453 0.035764731698298684 1
|
||||
224 1 3.3169425 0.9991016 0.0012967298400261944 1
|
||||
225 0 -2.2127771 0.0037472765 0.0054163318142264622 0
|
||||
226 1 3.46569 0.9993596 0.00092418675307120123 1
|
||||
227 1 2.595032 0.9953647 0.0067028341718725773 1
|
||||
228 0 -2.1765044 0.0040687565 0.0058819490307611549 0
|
||||
229 1 4.7208843 0.9999633 5.2971628937074883E-05 1
|
||||
230 1 1.957083 0.98047113 0.028452937881491717 1
|
||||
231 1 3.0528858 0.99836165 0.0023655819206737871 1
|
||||
232 0 0.26205277 0.5136947 1.0400657906256305 1
|
||||
233 1 2.1640766 0.98772305 0.017821513393369265 1
|
||||
234 0 -1.0094342 0.05511158 0.081784119876902178 0
|
||||
235 0 ? ? ? 0
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
668 1 0.97137 0.841662 0.24868712965444587 1
|
||||
669 1 2.965514 0.9980016 0.0028860018489550105 1
|
||||
670 1 2.3114579 0.99119353 0.012761320262412217 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
679 0 -2.1563811 0.004258803 0.0061572747374996819 0
|
||||
680 1 5.5908403 0.99999493 7.3092811775370979E-06 1
|
||||
681 1 3.6036851 0.9995323 0.00067493178171970517 1
|
||||
682 0 -1.4357655 0.021606723 0.031513604197463588 0
|
||||
683 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
684 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
685 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
686 0 -2.3047175 0.0030413263 0.0043943921550282242 0
|
||||
687 0 -1.7326522 0.01110462 0.016110195569449988 0
|
||||
688 0 -1.8597085 0.0083371075 0.012078323019740937 0
|
||||
689 0 -1.6691165 0.012811847 0.018603013766722361 0
|
||||
690 0 -2.1585593 0.0042378125 0.0061268624823746709 0
|
||||
691 1 1.8644311 0.9759922 0.035058472802235646 1
|
||||
692 0 -2.008045 0.0059607327 0.0086252515280869323 0
|
||||
693 0 -1.8253045 0.009010682 0.013058588214213173 0
|
||||
694 0 -1.7690041 0.010231109 0.014836397186509614 0
|
||||
695 0 -2.1563811 0.004258803 0.0061572747374996819 0
|
||||
696 1 2.527448 0.9945974 0.00781547081749355 1
|
||||
697 1 1.4814918 0.9444188 0.082501352535462646 1
|
||||
698 1 1.8482563 0.9751135 0.036357924898776386 1
|
||||
|
|
@ -0,0 +1 @@
|
|||
Saving predictor summary
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
maml.exe Train tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,} data=%Data% out=%Output% seed=1 xf=Term{col=Label}
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Beginning optimization
|
||||
num vars: 15
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 11 of 15 weights.
|
||||
Model trained with 150 training examples.
|
||||
Residual Deviance: 132.03748
|
||||
Null Deviance: 329.58368
|
||||
AIC: 154.03748
|
||||
Not training a calibrator because it is not needed.
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights
|
||||
Iris-setosa+(Bias) 2.2919443
|
||||
Iris-versicolor+(Bias) 0.76702803
|
||||
Iris-virginica+(Bias) -3.059023
|
||||
Iris-setosa+f3 -3.1749554
|
||||
Iris-setosa+f2 -2.887113
|
||||
Iris-setosa+f1 0.5143879
|
||||
Iris-setosa+f0 -0.058297563
|
||||
Iris-versicolor+f1 -0.70855236
|
||||
Iris-virginica+f3 3.1559532
|
||||
Iris-virginica+f2 1.9136102
|
||||
Iris-virginica+f0 0.033977978
|
||||
|
||||
*** MODEL STATISTICS SUMMARY ***
|
||||
Count of training examples: 150
|
||||
Residual Deviance: 132.03748
|
||||
Null Deviance: 329.58368
|
||||
AIC: 154.03748
|
||||
|
|
@ -0,0 +1 @@
|
|||
Saving predictor summary
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
maml.exe Train feat=Num lab=Lab tr=lr{t=- stat=+} loader=text{header+ col=Lab:0 col=Num:9-14} data=%Data% out=%Output%
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Beginning optimization
|
||||
num vars: 7
|
||||
improvement criterion: Mean Improvement
|
||||
Warning: Premature convergence occurred. The OptimizationTolerance may be set too small. ro equals zero. Is your function linear?
|
||||
L1 regularization selected 7 of 7 weights.
|
||||
Model trained with 500 training examples.
|
||||
Residual Deviance: 458.96985 (on 493 degrees of freedom)
|
||||
Null Deviance: 539.27637 (on 499 degrees of freedom)
|
||||
AIC: 472.96985
|
||||
Warning: The number of parameters is too large. Cannot hold the variance-covariance matrix in memory. Skipping computation of standard errors and z-statistics of coefficients. Consider choosing a larger L1 regularizerto reduce the number of parameters.
|
||||
Not training a calibrator because it is not needed.
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
Linear Binary Classification Predictor non-zero weights
|
||||
|
||||
(Bias) -4.4267545
|
||||
education-num 2.102785
|
||||
age 1.9203715
|
||||
hours-per-week 1.8823524
|
||||
capital-gain 1.6711036
|
||||
capital-loss 0.9768483
|
||||
fnlwgt 0.3191273
|
||||
|
||||
*** MODEL STATISTICS SUMMARY ***
|
||||
Count of training examples: 500
|
||||
Residual Deviance: 458.96985
|
||||
Null Deviance: 539.27637
|
||||
AIC: 472.96985
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 124 | 10 | 0.9254
|
||||
negative || 5 | 215 | 0.9773
|
||||
||======================
|
||||
Precision || 0.9612 | 0.9556 |
|
||||
OVERALL 0/1 ACCURACY: 0.957627
|
||||
LOG LOSS/instance: 0.166325
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.826202
|
||||
AUC: 0.994199
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 100 | 5 | 0.9524
|
||||
negative || 4 | 220 | 0.9821
|
||||
||======================
|
||||
Precision || 0.9615 | 0.9778 |
|
||||
OVERALL 0/1 ACCURACY: 0.972644
|
||||
LOG LOSS/instance: 0.182604
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.797882
|
||||
AUC: 0.961054
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.977627 (0.0166)
|
||||
Accuracy: 0.965136 (0.0075)
|
||||
Positive precision: 0.961389 (0.0001)
|
||||
Positive recall: 0.938877 (0.0135)
|
||||
Negative precision: 0.966667 (0.0111)
|
||||
Negative recall: 0.979708 (0.0024)
|
||||
Log-loss: 0.174464 (0.0081)
|
||||
Log-loss reduction: 0.812042 (0.0142)
|
||||
F1 Score: 0.949952 (0.0070)
|
||||
AUPRC: 0.979909 (0.0095)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LdSvm
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.977627 0.965136 0.961389 0.938877 0.966667 0.979708 0.174464 0.812042 0.949952 0.979909 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 3.526149 0.9999277 0.00010431124866801515 1
|
||||
6 0 -1.1218108 0.0914504 0.13836281897248148 0
|
||||
8 0 -1.6203799 0.027515754 0.040253216049532151 0
|
||||
9 0 -1.669663 0.024350794 0.035565573924712414 0
|
||||
10 0 -1.8151165 0.016943678 0.02465402036879874 0
|
||||
11 0 -1.8067945 0.017300142 0.02517724745610924 0
|
||||
18 1 2.0538619 0.9969428 0.0044173368194155311 1
|
||||
20 1 1.8093374 0.99431825 0.0082204124602030648 1
|
||||
21 1 2.1534398 0.9976257 0.0034294513286414175 1
|
||||
25 1 -0.6382538 0.25631282 1.9640224701655145 0
|
||||
28 0 -1.8067945 0.017300142 0.02517724745610924 0
|
||||
31 0 -1.728947 0.021011736 0.030636530178365998 0
|
||||
32 1 2.5068038 0.9990328 0.0013960564827661087 1
|
||||
35 0 -1.8067945 0.017300142 0.02517724745610924 0
|
||||
37 0 -1.1458213 0.08649767 0.13051968926295085 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 -0.20914128 0.50675654 0.98063528051212701 0
|
||||
44 1 2.8598988 0.999606 0.00056851466206539339 1
|
||||
45 0 -1.7839876 0.018315233 0.026668266391998201 0
|
||||
46 1 1.2672604 0.9777957 0.0323950043331281 1
|
||||
48 0 -1.5373471 0.033772714 0.049565500871283592 0
|
||||
50 1 0.9638393 0.95314443 0.069233250884230338 1
|
||||
51 1 -0.85461456 0.1657656 2.5927834616592653 0
|
||||
52 1 1.3377451 0.98137456 0.027124219099611876 1
|
||||
54 1 2.3109157 0.99840856 0.0022977972822397406 1
|
||||
56 1 1.6193739 0.9908179 0.013308155996179006 1
|
||||
60 1 -0.41534865 0.37804702 1.4033624154398172 0
|
||||
63 1 -0.7428849 0.20890367 2.2590902549815062 0
|
||||
64 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
66 0 -1.6305369 0.026832342 0.039239720183993521 0
|
||||
68 1 2.9473996 0.9996847 0.00045496584261690929 1
|
||||
69 0 -1.7663403 0.019140588 0.027881727370439507 0
|
||||
70 0 -1.4818044 0.03870289 0.05694569877155152 0
|
||||
71 1 2.8194919 0.9995634 0.00063002400430274086 1
|
||||
72 0 -1.0894251 0.09853413 0.14965522272949733 0
|
||||
73 1 2.1321487 0.9974938 0.0036202156435655515 1
|
||||
74 1 0.8495918 0.9383063 0.091869185987172225 1
|
||||
76 0 -1.6012264 0.028850798 0.042235133868308392 0
|
||||
77 0 -1.5217263 0.035094522 0.051540471690820183 0
|
||||
79 0 -1.7627866 0.019311147 0.028132615082685872 0
|
||||
82 0 -1.5285081 0.03451464 0.050673710939356742 0
|
||||
88 0 -1.6305369 0.026832342 0.039239720183993521 0
|
||||
90 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
91 0 -1.7983022 0.017671494 0.02572252962241716 0
|
||||
92 0 -1.6305369 0.026832342 0.039239720183993521 0
|
||||
93 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
95 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
96 0 -1.8405538 0.015898272 0.023120638748319003 0
|
||||
97 0 -1.5770282 0.030627552 0.04487701744231537 0
|
||||
98 1 2.854033 0.9996001 0.00057703118380684083 1
|
||||
99 1 2.7723851 0.9995078 0.00071029118780069692 1
|
||||
100 1 1.6455318 0.99140424 0.01245467275034115 1
|
||||
102 0 -1.5401378 0.033541683 0.049220584188514341 0
|
||||
104 1 4.042039 0.99998057 2.8033444452050102E-05 1
|
||||
105 1 -0.17070323 0.5311762 0.91273756280616514 0
|
||||
106 1 2.352503 0.9985682 0.002067163879089538 1
|
||||
108 0 -1.8354541 0.016102642 0.023420275836834568 0
|
||||
109 1 1.7236166 0.9929427 0.010217642001234576 1
|
||||
111 1 1.080668 0.9647726 0.051739187127587655 1
|
||||
112 1 2.1199894 0.99741524 0.003733841442281771 1
|
||||
113 1 3.2178142 0.9998416 0.00022858305058355181 1
|
||||
115 0 -1.6449932 0.025888044 0.037840502738395523 0
|
||||
117 1 2.5524666 0.99913883 0.0012429379358114413 1
|
||||
120 0 -1.6473104 0.025739722 0.037620848572117747 0
|
||||
121 0 -1.5082824 0.036271907 0.053301934714094418 0
|
||||
122 1 3.113542 0.9997934 0.00029807672479589532 1
|
||||
123 1 1.3110919 0.9800931 0.029009264450288733 1
|
||||
125 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
128 1 1.1243547 0.9683626 0.046380690271674324 1
|
||||
129 0 -1.5529418 0.032501105 0.047668081687226582 0
|
||||
131 0 -1.728947 0.021011736 0.030636530178365998 0
|
||||
132 1 2.495432 0.9990044 0.0014370285632838195 1
|
||||
133 0 -1.7545773 0.019710878 0.028720780875823325 0
|
||||
137 0 -1.840337 0.015906906 0.023133295326727574 0
|
||||
138 0 -1.6268921 0.027075663 0.039600482255918001 0
|
||||
141 0 -1.8808964 0.014369044 0.020880527660383778 0
|
||||
144 0 -1.8067945 0.017300142 0.02517724745610924 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -1.7361412 0.020638343 0.030086380627492772 0
|
||||
150 0 -1.8151165 0.016943678 0.02465402036879874 0
|
||||
151 1 1.4145024 0.9846293 0.02234746278737338 1
|
||||
152 1 2.7128947 0.9994274 0.0008263552805391624 1
|
||||
154 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
156 0 -1.7267356 0.021127839 0.030807635688311862 0
|
||||
161 0 -1.6207669 0.02748941 0.040214135762092207 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 2.826551 0.99957114 0.00061884029247210139 1
|
||||
169 0 -1.9158832 0.013160857 0.019113154122289679 0
|
||||
171 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
173 1 4.37442 0.99999166 1.2038835790403619E-05 1
|
||||
174 1 1.5860469 0.9900131 0.014480446740836076 1
|
||||
176 0 -1.728947 0.021011736 0.030636530178365998 0
|
||||
177 1 2.0442426 0.9968673 0.004526626072621099 1
|
||||
179 1 0.69324994 0.9108409 0.13472897458542998 1
|
||||
180 0 -1.8151165 0.016943678 0.02465402036879874 0
|
||||
181 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
183 1 2.46993 0.9989377 0.0015333519660518796 1
|
||||
187 1 3.709254 0.99995464 6.5440882835427928E-05 1
|
||||
188 1 2.3962395 0.9987188 0.0018495686286770713 1
|
||||
189 0 -1.5609277 0.03186796 0.046724267972077176 0
|
||||
191 1 3.1068437 0.99978983 0.00030323727966705274 1
|
||||
192 0 -1.6642753 0.024678733 0.036050579948018802 0
|
||||
196 0 1.7809702 0.9938954 7.3558902337739598 1
|
||||
198 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
199 0 -1.7625268 0.019323679 0.028151050890976172 0
|
||||
201 1 3.121021 0.9997973 0.00029248614451634898 1
|
||||
202 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
204 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
205 1 3.3835957 0.9998961 0.00014989066649957348 1
|
||||
206 1 1.9998912 0.99649423 0.0050666382112453936 1
|
||||
207 0 -1.8151165 0.016943678 0.02465402036879874 0
|
||||
209 0 -1.6287832 0.026949149 0.039412893057398792 0
|
||||
210 1 3.9105215 0.9999728 3.9212577292531158E-05 1
|
||||
211 1 2.608784 0.99925375 0.0010770133050728963 1
|
||||
212 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
216 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
218 1 2.4702137 0.99893844 0.0015323189731946969 1
|
||||
219 0 -1.3306053 0.055855475 0.082920378294568234 0
|
||||
223 1 1.5743525 0.98971444 0.014915761722911095 1
|
||||
226 1 2.5786104 0.9991942 0.0011629853554646094 1
|
||||
228 0 -1.8151165 0.016943678 0.02465402036879874 0
|
||||
233 1 1.6287805 0.9910332 0.012994710986576039 1
|
||||
237 1 2.1128998 0.9973683 0.0038017798062689964 1
|
||||
239 1 1.3779497 0.9831558 0.024508056437645195 1
|
||||
240 0 -1.2835051 0.06252495 0.093147802895663309 0
|
||||
241 0 -1.640072 0.026205814 0.038311207086098323 0
|
||||
242 0 -1.728947 0.021011736 0.030636530178365998 0
|
||||
244 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
246 1 3.0781894 0.9997739 0.00032620197268999553 1
|
||||
247 1 0.6641216 0.90463364 0.14459444741243144 1
|
||||
248 0 -1.5792731 0.030458353 0.044625224439241717 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -1.8023863 0.017491939 0.025458850191372704 0
|
||||
252 0 0.9689717 0.9537244 4.433604004647302 1
|
||||
254 1 2.3050709 0.9983847 0.0023322490511725183 1
|
||||
257 0 -1.7625268 0.019323679 0.028151050890976172 0
|
||||
258 0 -1.6812668 0.023658853 0.034542761592575993 0
|
||||
259 0 1.0347145 0.9605738 4.664701247345338 1
|
||||
260 1 2.4606466 0.99891233 0.0015700236916840443 1
|
||||
262 1 2.6361723 0.99930394 0.0010045563562377736 1
|
||||
267 1 0.9629435 0.9530425 0.069387532884252923 1
|
||||
268 1 2.8230937 0.9995674 0.00062426008045563658 1
|
||||
269 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
271 0 -1.5770282 0.030627552 0.04487701744231537 0
|
||||
272 1 0.9629435 0.9530425 0.069387532884252923 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -1.7625268 0.019323679 0.028151050890976172 0
|
||||
277 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
278 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
279 1 1.9776616 0.9962909 0.0053610165703118166 1
|
||||
280 0 -1.6812668 0.023658853 0.034542761592575993 0
|
||||
283 1 1.4362993 0.98544675 0.021150177930043857 1
|
||||
284 1 1.5239974 0.98832434 0.016943517734770815 1
|
||||
285 1 4.086542 0.99998266 2.5023692718535918E-05 1
|
||||
288 1 -0.55413014 0.29920298 1.7408035573425691 0
|
||||
290 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
291 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
293 1 1.0838963 0.9650508 0.051323182730306137 1
|
||||
296 0 -0.4461656 0.35978752 0.64337730295594198 0
|
||||
297 0 ? ? ? 0
|
||||
299 1 1.7776226 0.9938435 0.0089094116551614033 1
|
||||
300 1 2.1375132 0.9975277 0.0035711644793204413 1
|
||||
301 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
303 0 -1.7732897 0.018811302 0.027397479222316581 0
|
||||
304 1 1.2487937 0.9767519 0.033935903311972507 1
|
||||
308 1 2.3022134 0.9983729 0.0023493029812132662 1
|
||||
309 0 -1.2047112 0.07536439 0.11304316719528537 0
|
||||
311 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
312 1 0.27813435 0.78028435 0.35792813939514623 1
|
||||
314 0 -1.8861424 0.014181155 0.020605534607128398 0
|
||||
316 1 1.0557021 0.9625479 0.055069761039477909 1
|
||||
317 1 2.593145 0.9992235 0.0011207301839781217 1
|
||||
319 0 1.0927777 0.9658053 4.8700831254760413 1
|
||||
321 0 ? ? ? 0
|
||||
323 1 1.2560339 0.9771668 0.033323288636255034 1
|
||||
327 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
328 1 1.1168995 0.96777606 0.047254843207145611 1
|
||||
329 1 2.1218667 0.9974275 0.0037160814329960466 1
|
||||
331 0 -1.5045743 0.03660329 0.053798098638008135 0
|
||||
332 0 -1.4665028 0.04017828 0.05916163491234961 0
|
||||
333 1 1.257413 0.9772449 0.033207924377497325 1
|
||||
336 1 1.3071736 0.9798976 0.029297073512464186 1
|
||||
338 0 -1.8861424 0.014181155 0.020605534607128398 0
|
||||
343 0 -1.917501 0.013107483 0.019035126752815424 0
|
||||
344 1 3.0205958 0.9997383 0.00037763733050068021 1
|
||||
346 0 -1.4918919 0.037758835 0.055529574659258722 0
|
||||
347 0 -1.9453821 0.012220488 0.017739049375469829 0
|
||||
348 1 -0.57922256 0.2859848 1.8059895531549937 0
|
||||
349 1 1.0008829 0.95717996 0.063137897273113183 1
|
||||
350 0 -1.6579802 0.025067361 0.036625552785426119 0
|
||||
352 0 -0.40991142 0.38130656 0.69270335504735825 0
|
||||
353 1 2.4987164 0.99901265 0.0014251499831937128 1
|
||||
354 0 -1.8474231 0.015627015 0.022723030236888715 0
|
||||
355 0 -1.6562978 0.02517223 0.036780744854002068 0
|
||||
358 1 1.9056497 0.99554807 0.0064371175433691613 1
|
||||
360 1 4.552837 0.9999947 7.6532482629398447E-06 1
|
||||
361 1 1.8474804 0.9948413 0.0074617263027793035 1
|
||||
366 1 3.9241662 0.9999738 3.7836679345189628E-05 1
|
||||
368 0 -1.762107 0.019343935 0.028180850648229314 0
|
||||
370 0 -1.479331 0.0389378 0.057298289010378166 0
|
||||
371 0 -1.762107 0.019343935 0.028180850648229314 0
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
637 0 -1.684458 0.008274149 0.011986732622814649 0
|
||||
640 0 -1.6729596 0.008540397 0.012374103200670806 0
|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
669 1 1.2057604 0.9623841 0.05531528053054037 1
|
||||
671 0 -1.6937388 0.008065268 0.011682899165320948 0
|
||||
672 0 -1.7022704 0.007877869 0.011410365888835546 0
|
||||
673 0 -1.6916316 0.008112232 0.011751206129679637 0
|
||||
674 0 -1.6947328 0.00804321 0.011650817261510122 0
|
||||
675 0 -1.6815659 0.008340332 0.012083013727497736 0
|
||||
676 0 -1.6972513 0.007987585 0.011569918767056894 0
|
||||
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|
||||
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|
||||
686 0 -1.6837752 0.008289728 0.01200939630472476 0
|
||||
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|
||||
690 0 -1.6821339 0.008327292 0.012064043688707938 0
|
||||
695 0 -1.6808038 0.008357859 0.012108513451261255 0
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 16 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 232 | 7 | 0.9707
|
||||
negative || 11 | 433 | 0.9752
|
||||
||======================
|
||||
Precision || 0.9547 | 0.9841 |
|
||||
OVERALL 0/1 ACCURACY: 0.973646
|
||||
LOG LOSS/instance: 0.111359
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.880772
|
||||
AUC: 0.996136
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996136 (0.0000)
|
||||
Accuracy: 0.973646 (0.0000)
|
||||
Positive precision: 0.954733 (0.0000)
|
||||
Positive recall: 0.970711 (0.0000)
|
||||
Negative precision: 0.984091 (0.0000)
|
||||
Negative recall: 0.975225 (0.0000)
|
||||
Log-loss: 0.111359 (0.0000)
|
||||
Log-loss reduction: 0.880772 (0.0000)
|
||||
F1 Score: 0.962656 (0.0000)
|
||||
AUPRC: 0.992151 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LdSvm
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996136 0.973646 0.954733 0.970711 0.984091 0.975225 0.111359 0.880772 0.962656 0.992151 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -1.6978589 0.014107211 0.020497325114984831 0
|
||||
1 0 1.2372239 0.90693176 3.425567232731229 1
|
||||
2 0 -1.8189844 0.0108135985 0.015685688008012112 0
|
||||
3 0 1.1770016 0.89500177 3.2515630740820534 1
|
||||
4 0 -1.7234358 0.0133380145 0.019372169618365857 0
|
||||
5 1 4.017951 0.9997877 0.00030633362145029628 1
|
||||
6 0 -0.97414255 0.06671321 0.099607613998291863 0
|
||||
7 0 -1.9396354 0.008291152 0.012011467865383887 0
|
||||
8 0 -1.8314458 0.01052131 0.015259458572543063 0
|
||||
9 0 -1.825638 0.010656549 0.015456654263451611 0
|
||||
10 0 -2.0861132 0.006001023 0.0086837280459640571 0
|
||||
11 0 -2.0513897 0.0064793876 0.0093781958119407548 0
|
||||
12 1 -0.30384433 0.24076566 2.0542984512779223 0
|
||||
13 0 -1.9125963 0.00880019 0.012752183870291556 0
|
||||
14 1 3.0720708 0.99826485 0.0025054677860249187 1
|
||||
15 1 0.6518165 0.72623265 0.46149630672300462 1
|
||||
16 0 -1.8843536 0.009364972 0.013574460402384178 0
|
||||
17 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
18 1 2.6420202 0.99549955 0.0065074292106389094 1
|
||||
19 0 -1.553345 0.019347388 0.028185931054466484 0
|
||||
20 1 2.2590454 0.98952115 0.015197557251184228 1
|
||||
21 1 2.756348 0.9965059 0.0050497247154917095 1
|
||||
22 0 -1.9765549 0.0076429537 0.011068804607153504 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
25 1 0.4178614 0.61196905 0.70846939480775462 1
|
||||
26 0 -1.9519182 0.008069666 0.011689295313516538 0
|
||||
27 0 -1.7729106 0.011965697 0.017366963662627737 0
|
||||
28 0 -2.0513897 0.0064793876 0.0093781958119407548 0
|
||||
29 0 -2.041393 0.006623999 0.0095882021162957791 0
|
||||
30 0 -1.9953357 0.007332774 0.010617933717007349 0
|
||||
31 0 -2.0087452 0.007118983 0.010307254021120166 0
|
||||
32 1 2.8048484 0.9968619 0.0045344758956720795 1
|
||||
33 0 -1.9751817 0.0076661366 0.011102508518205444 0
|
||||
34 0 -1.9021913 0.009004226 0.013049189621385557 0
|
||||
35 0 -2.0513897 0.0064793876 0.0093781958119407548 0
|
||||
36 1 3.2734656 0.9988903 0.0016018754774104063 1
|
||||
37 0 -1.0911664 0.052232217 0.077394473966496305 0
|
||||
38 1 1.7112017 0.96545076 0.050725408755079915 1
|
||||
39 1 0.7522764 0.7683249 0.3802115636035453 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.2552793 0.9102642 0.13564276268297687 1
|
||||
42 1 3.0560913 0.9982022 0.0025960045999252148 1
|
||||
43 1 0.45132008 0.62947446 0.6677802485119535 1
|
||||
44 1 3.2003763 0.9986948 0.0018842680064854573 1
|
||||
45 0 -2.0555298 0.006420419 0.0092925698962792076 0
|
||||
46 1 1.5603673 0.95234597 0.070442325318680096 1
|
||||
47 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
48 0 -1.7234358 0.0133380145 0.019372169618365857 0
|
||||
49 1 2.0412462 0.9831063 0.024580653880321747 1
|
||||
50 1 1.2093117 0.90156144 0.149502284012687 1
|
||||
51 1 -0.17043188 0.2990218 1.7416773781754302 0
|
||||
52 1 1.974623 0.9804627 0.028465391915567968 1
|
||||
53 1 2.8347442 0.997063 0.0042434571723844296 1
|
||||
54 1 2.6006944 0.9950688 0.007131833111937198 1
|
||||
55 1 1.7887777 0.9707626 0.042809551682713186 1
|
||||
56 1 2.228299 0.9887883 0.016266412675380402 1
|
||||
57 1 0.6835461 0.74002814 0.43434795807526866 1
|
||||
58 1 0.77842 0.77850646 0.36121908820280463 1
|
||||
59 1 0.6561758 0.7281548 0.45768294884224175 1
|
||||
60 1 0.7864456 0.7815671 0.35555836249945261 1
|
||||
61 0 -2.040679 0.0066344505 0.0096033807805429999 0
|
||||
62 1 2.6868002 0.9959242 0.0058921887636822108 1
|
||||
63 1 0.3141208 0.5560188 0.8467943548455974 1
|
||||
64 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
65 1 1.5602338 0.95233256 0.070462641658664268 1
|
||||
66 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
67 1 1.5825492 0.95453423 0.067131156723470822 1
|
||||
68 1 3.5415878 0.9993882 0.00088288505839940166 1
|
||||
69 0 -2.0450644 0.00657052 0.0095105353553682197 0
|
||||
70 0 -1.5562137 0.019226782 0.028008511067292775 0
|
||||
71 1 2.9843981 0.9978923 0.003043948220772294 1
|
||||
72 0 -1.4067742 0.026599878 0.03889514011232937 0
|
||||
73 1 2.8505585 0.99716413 0.0040971074627124873 1
|
||||
74 1 1.1689104 0.89329976 0.16278372307983863 1
|
||||
75 0 -1.8307506 0.010537409 0.015282931446431586 0
|
||||
76 0 -1.9335465 0.008403172 0.012174438152180887 0
|
||||
77 0 -1.5650593 0.018859526 0.027468387159428068 0
|
||||
78 0 -1.7008895 0.014013834 0.020360690487667012 0
|
||||
79 0 -2.0366454 0.0066937967 0.0096895736529440148 0
|
||||
80 0 -1.6463667 0.01579076 0.02296303473554389 0
|
||||
81 0 -1.8412149 0.0102976505 0.014933391337286264 0
|
||||
82 0 -1.6200093 0.016727522 0.024336832782493923 0
|
||||
83 0 -1.5062243 0.02143779 0.031264525228513272 0
|
||||
84 1 3.1557448 0.9985589 0.002080597823340882 1
|
||||
85 1 2.747295 0.99643517 0.005152157952757217 1
|
||||
86 1 0.82808256 0.79695415 0.3274313597585351 1
|
||||
87 1 2.1869607 0.98772264 0.017822122814606961 1
|
||||
88 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
89 0 -2.003984 0.0071941745 0.01041651400589709 0
|
||||
90 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
91 0 -1.9907031 0.007408104 0.010727418926693355 0
|
||||
92 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
93 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
94 0 -2.0087452 0.007118983 0.010307254021120166 0
|
||||
95 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
96 0 -2.0620565 0.006328539 0.0091591645502576199 0
|
||||
97 0 -1.6978589 0.014107211 0.020497325114984831 0
|
||||
98 1 3.4724586 0.99928665 0.0010295114265942056 1
|
||||
99 1 3.3775587 0.9991193 0.0012711676770513272 1
|
||||
100 1 2.1504493 0.98669857 0.019318679975128518 1
|
||||
101 1 -0.516132 0.16516036 2.5980606445036369 0
|
||||
102 0 -1.7245102 0.013306623 0.019326270263535259 0
|
||||
103 1 0.54142445 0.674856 0.56734838532350829 1
|
||||
104 1 4.390837 0.9999073 0.00013372270820105733 1
|
||||
105 1 0.92749274 0.83037925 0.26815770570061948 1
|
||||
106 1 3.018927 0.9980477 0.002819312828400164 1
|
||||
107 1 2.1112907 0.98550653 0.021062657540737651 1
|
||||
108 0 -2.0518284 0.0064731142 0.0093690862289939215 0
|
||||
109 1 2.162655 0.98705 0.018804931801562962 1
|
||||
110 0 -1.50819 0.021346323 0.031129681582153831 0
|
||||
111 1 1.4278185 0.9370476 0.093805758191658123 1
|
||||
112 1 2.7404418 0.9963806 0.0052311236685881612 1
|
||||
113 1 3.4637341 0.9992727 0.0010496479014940139 1
|
||||
114 0 -1.4268851 0.02546668 0.037216582552397091 0
|
||||
115 0 -1.8295213 0.010565935 0.015324523883477522 0
|
||||
116 0 -0.34026653 0.22627957 0.37011572941870247 0
|
||||
117 1 2.8828368 0.99736 0.0038137641897377835 1
|
||||
118 0 -1.9712849 0.0077323075 0.011198713702499781 0
|
||||
119 0 -1.7558248 0.012423083 0.018034978928514432 0
|
||||
120 0 -1.9876262 0.007458561 0.010800758427813049 0
|
||||
121 0 -1.6242334 0.016573802 0.024111305983288409 0
|
||||
122 1 3.617287 0.99948287 0.00074625371113042267 1
|
||||
123 1 1.3860625 0.9313457 0.10261132145527939 1
|
||||
124 1 2.5432146 0.9944005 0.0081010712720367452 1
|
||||
125 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
126 1 2.6429915 0.99550927 0.00649334932719725 1
|
||||
127 0 -1.9240623 0.008580656 0.012432686470162331 0
|
||||
128 1 1.7498778 0.96820617 0.046613811067734712 1
|
||||
129 0 -2.0218792 0.006915586 0.010011740292571538 0
|
||||
130 0 -1.5562137 0.019226782 0.028008511067292775 0
|
||||
131 0 -2.0087452 0.007118983 0.010307254021120166 0
|
||||
132 1 2.9439168 0.9976943 0.0033302431670292525 1
|
||||
133 0 -1.9336952 0.00840042 0.012170434130479442 0
|
||||
134 0 -1.9544495 0.008024756 0.011623977914128026 0
|
||||
135 0 -1.4032172 0.026805347 0.03919970090442193 0
|
||||
136 0 -1.8843536 0.009364972 0.013574460402384178 0
|
||||
137 0 -2.0231707 0.0068959 0.0099831414740292716 0
|
||||
138 0 -1.7963188 0.011365962 0.016491516855350056 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -2.0231707 0.0068959 0.0099831414740292716 0
|
||||
141 0 -2.0886197 0.005967883 0.0086356290226746457 0
|
||||
142 1 1.6502461 0.9606354 0.057939085607541567 1
|
||||
143 0 -1.8295213 0.010565935 0.015324523883477522 0
|
||||
144 0 -2.0513897 0.0064793876 0.0093781958119407548 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 0.46527413 0.6366787 0.65136260481811814 1
|
||||
147 0 -2.0273747 0.006832203 0.0098906111738996227 0
|
||||
148 0 -0.6634741 0.12479242 0.19230286077404418 0
|
||||
149 1 4.0182796 0.99978787 0.00030607559271454053 1
|
||||
150 0 -2.0861132 0.006001023 0.0086837280459640571 0
|
||||
151 1 1.7295094 0.9667826 0.048736542940169436 1
|
||||
152 1 3.3277786 0.99901634 0.0014198132616608289 1
|
||||
153 0 -1.7236159 0.013332747 0.01936446742327708 0
|
||||
154 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
155 1 1.1383023 0.8866398 0.17358001295845912 1
|
||||
156 0 -2.023841 0.006885706 0.009968332898987178 0
|
||||
157 0 -2.0087452 0.007118983 0.010307254021120166 0
|
||||
158 0 ? ? ? 0
|
||||
159 1 4.238142 0.9998698 0.00018781727970963838 1
|
||||
160 1 3.0956872 0.9983534 0.0023774682464645079 1
|
||||
161 0 -1.7637271 0.012209432 0.017722902089766578 0
|
||||
162 0 -1.9240623 0.008580656 0.012432686470162331 0
|
||||
163 0 -1.5453587 0.01968706 0.028685728789663269 0
|
||||
164 0 ? ? ? 0
|
||||
165 0 -1.63414 0.016218709 0.023590475677062198 0
|
||||
166 1 2.733293 0.9963229 0.0053147543687647545 1
|
||||
167 1 3.1545997 0.9985552 0.0020859369897684504 1
|
||||
168 0 -1.9240623 0.008580656 0.012432686470162331 0
|
||||
169 0 -2.1221418 0.0055417684 0.0080173174085991696 0
|
||||
170 0 -2.0231707 0.0068959 0.0099831414740292716 0
|
||||
171 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
172 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
173 1 4.87563 0.9999684 4.5576122367809359E-05 1
|
||||
174 1 1.9599049 0.9798261 0.029402383857982356 1
|
||||
175 1 2.7358375 0.99634355 0.00528480556290688 1
|
||||
176 0 -2.0087452 0.007118983 0.010307254021120166 0
|
||||
177 1 2.1451032 0.98654175 0.01954799130214905 1
|
||||
178 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
179 1 0.7522323 0.76830745 0.38024435668928819 1
|
||||
180 0 -2.0861132 0.006001023 0.0086837280459640571 0
|
||||
181 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
182 0 -1.553345 0.019347388 0.028185931054466484 0
|
||||
183 1 2.8846703 0.99737066 0.0037983310812710037 1
|
||||
184 1 2.071973 0.98420393 0.022970810773688635 1
|
||||
185 0 -2.024538 0.0068751182 0.0099529521924001715 0
|
||||
186 1 1.7864907 0.97061795 0.043024554296281631 1
|
||||
187 1 4.4870605 0.99992514 0.0001080091477601344 1
|
||||
188 1 2.761541 0.99654585 0.0049919096712559259 1
|
||||
189 0 -1.9060429 0.008928156 0.01293845163793087 0
|
||||
190 1 4.0173116 0.9997874 0.00030676366944577566 1
|
||||
191 1 3.7018433 0.99957144 0.00061841015144072802 1
|
||||
192 0 -1.7729106 0.011965697 0.017366963662627737 0
|
||||
193 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
194 0 -1.9240623 0.008580656 0.012432686470162331 0
|
||||
195 0 -1.8210216 0.010765272 0.015615207407795869 0
|
||||
196 0 2.228295 0.98878825 6.4788443371234399 1
|
||||
197 0 -1.5236466 0.020640321 0.030089294607200185 0
|
||||
198 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
199 0 -1.9765549 0.0076429537 0.011068804607153504 0
|
||||
200 1 3.4169302 0.999193 0.0011647065699455159 1
|
||||
201 1 3.4380128 0.9992299 0.0011114359334885055 1
|
||||
202 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
203 0 -1.6978589 0.014107211 0.020497325114984831 0
|
||||
204 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
205 1 3.921336 0.99973685 0.00037970166407319078 1
|
||||
206 1 2.4813254 0.9935801 0.0092918119341376844 1
|
||||
207 0 -2.0861132 0.006001023 0.0086837280459640571 0
|
||||
208 0 -2.0861132 0.006001023 0.0086837280459640571 0
|
||||
209 0 -1.7381499 0.012914405 0.018752901965595994 0
|
||||
210 1 4.6597543 0.999949 7.3610452445895346E-05 1
|
||||
211 1 3.1483767 0.9985351 0.0021149582883654753 1
|
||||
212 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
213 1 4.7814918 0.9999611 5.6153428310976729E-05 1
|
||||
214 1 4.2807555 0.9998816 0.00017078888105812674 1
|
||||
215 1 2.6910355 0.9959622 0.0058371028247891357 1
|
||||
216 0 -2.1107786 0.00568271 0.0082218009135742332 0
|
||||
217 0 -2.0770245 0.006122732 0.0088603878483536503 0
|
||||
218 1 2.749413 0.9964518 0.0051280807418853849 1
|
||||
219 0 -1.2881747 0.03434712 0.050423414388079207 0
|
||||
220 0 -2.049355 0.0065085646 0.0094205643780053885 0
|
||||
221 1 3.5677118 0.99942267 0.00083315250348971811 1
|
||||
222 1 -1.282123 0.034796055 4.844932423019034 0
|
||||
223 1 1.8505825 0.97441924 0.03738548086301631 1
|
||||
224 1 3.153092 0.99855036 0.0020929123821295226 1
|
||||
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680 1 5.349205 0.999989 1.5908482915272255E-05 1
|
||||
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|
||||
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|
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684 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
685 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
686 0 -2.1395137 0.0053329915 0.0077144692947309444 0
|
||||
687 0 -1.886754 0.009315605 0.013502567934296442 0
|
||||
688 0 -1.9413326 0.008260193 0.011966430720833556 0
|
||||
689 0 -1.7011598 0.014005533 0.020348544693246109 0
|
||||
690 0 -2.0950174 0.0058841174 0.00851406056433982 0
|
||||
691 1 1.7261217 0.96654 0.049098687039799346 1
|
||||
692 0 -2.0231707 0.0068959 0.0099831414740292716 0
|
||||
693 0 -1.9052485 0.008943793 0.01296121433157176 0
|
||||
694 0 -1.9114863 0.008821737 0.012783546065593747 0
|
||||
695 0 -2.0886197 0.005967883 0.0086356290226746457 0
|
||||
696 1 2.564204 0.99465436 0.0077328189280636388 1
|
||||
697 1 1.6150802 0.957571 0.062548589215837253 1
|
||||
698 1 1.9523501 0.9794915 0.029895162889108172 1
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 121 | 13 | 0.9030
|
||||
negative || 14 | 206 | 0.9364
|
||||
||======================
|
||||
Precision || 0.8963 | 0.9406 |
|
||||
OVERALL 0/1 ACCURACY: 0.923729
|
||||
LOG LOSS/instance: 0.332232
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.652840
|
||||
AUC: 0.969878
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 102 | 3 | 0.9714
|
||||
negative || 54 | 170 | 0.7589
|
||||
||======================
|
||||
Precision || 0.6538 | 0.9827 |
|
||||
OVERALL 0/1 ACCURACY: 0.826748
|
||||
LOG LOSS/instance: 0.402185
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.554836
|
||||
AUC: 0.968878
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.969378 (0.0005)
|
||||
Accuracy: 0.875238 (0.0485)
|
||||
Positive precision: 0.775071 (0.1212)
|
||||
Positive recall: 0.937207 (0.0342)
|
||||
Negative precision: 0.961649 (0.0210)
|
||||
Negative recall: 0.847646 (0.0887)
|
||||
Log-loss: 0.367208 (0.0350)
|
||||
Log-loss reduction: 0.603838 (0.0490)
|
||||
F1 Score: 0.840619 (0.0590)
|
||||
AUPRC: 0.962365 (0.0123)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LdSvm
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.969378 0.875238 0.775071 0.937207 0.961649 0.847646 0.367208 0.603838 0.840619 0.962365 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 3.8534284 0.9966877 0.00478655555839757 1
|
||||
6 0 1.9441669 0.9089837 3.4577313685410616 1
|
||||
8 0 -1.3064579 0.029403893 0.04305702174461324 0
|
||||
9 0 -0.92505693 0.056439757 0.083813463651964668 0
|
||||
10 0 -0.75773776 0.07460297 0.11185562463682144 0
|
||||
11 0 -0.9906934 0.050519254 0.074789349927547044 0
|
||||
18 1 2.413366 0.95844084 0.061238710589369388 1
|
||||
20 1 1.568261 0.83627826 0.25794503634600341 1
|
||||
21 1 1.7287251 0.8718046 0.19792328720715538 1
|
||||
25 1 2.2633896 0.9463779 0.079511751428780225 1
|
||||
28 0 -0.9906934 0.050519254 0.074789349927547044 0
|
||||
31 0 -1.2457733 0.032655496 0.04789832154160352 0
|
||||
32 1 -0.24883446 0.16654387 2.5860258312595557 0
|
||||
35 0 -0.9906934 0.050519254 0.074789349927547044 0
|
||||
37 0 -2.2412913 0.0056849807 0.0082250953708129255 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 -0.36874965 0.13892916 2.8475786709803779 0
|
||||
44 1 1.0931432 0.68639857 0.54288155656014681 1
|
||||
45 0 -0.97998923 0.051442977 0.076193590341433137 0
|
||||
46 1 4.6290503 0.9991675 0.001201541052029201 1
|
||||
48 0 -1.5960495 0.017752413 0.025841375908303817 0
|
||||
50 1 -0.026029551 0.2291866 2.1254054340117103 0
|
||||
51 1 1.164235 0.7130289 0.48796752697067669 1
|
||||
52 1 1.5556282 0.8331697 0.26331772347354976 1
|
||||
54 1 -0.39825928 0.1327513 2.9132020957225473 0
|
||||
56 1 4.295429 0.9984916 0.0021778254179274439 1
|
||||
60 1 1.99921 0.91678673 0.12534193242882088 1
|
||||
63 1 0.5764582 0.46548873 1.1031818501641952 1
|
||||
64 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
66 0 -1.5475866 0.019324278 0.028151933228968792 0
|
||||
68 1 1.4756343 0.8123842 0.29976593484859421 1
|
||||
69 0 -0.8957717 0.059286844 0.088173214102917494 0
|
||||
70 0 -1.3289838 0.028278641 0.041385415007353897 0
|
||||
71 1 -0.122156024 0.2003083 2.3197059424707156 0
|
||||
72 0 -1.1036713 0.041683324 0.061425620700316604 0
|
||||
73 1 2.9439125 0.9834473 0.024080330501790009 1
|
||||
74 1 0.297962 0.34637478 1.5295942057597371 1
|
||||
76 0 -0.7205589 0.07931232 0.11921624878485172 0
|
||||
77 0 -0.6583719 0.087799355 0.13257690520182666 0
|
||||
79 0 -1.3194588 0.028749254 0.042084292665898589 0
|
||||
82 0 -1.3070143 0.02937558 0.043014936297614569 0
|
||||
88 0 -1.5475866 0.019324278 0.028151933228968792 0
|
||||
90 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
91 0 -0.90768975 0.05811229 0.086373019689586619 0
|
||||
92 0 -1.5475866 0.019324278 0.028151933228968792 0
|
||||
93 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
95 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
96 0 -0.76303005 0.07395388 0.11084405177396785 0
|
||||
97 0 -1.6949314 0.014924774 0.021694193556481233 0
|
||||
98 1 0.18992028 0.3041264 1.7172569830896267 1
|
||||
99 1 3.3184137 0.9914438 0.012397080601365768 1
|
||||
100 1 -0.3125569 0.15135887 2.7239548465080152 0
|
||||
102 0 -1.5010574 0.020961441 0.030562414181111056 0
|
||||
104 1 -0.31568554 0.15064345 2.7307901204403953 0
|
||||
105 1 -1.0048026 0.049325664 4.3415177295854761 0
|
||||
106 1 4.2012625 0.9982162 0.0025757603852759247 1
|
||||
108 0 -0.42943838 0.12647834 0.19508462491642881 0
|
||||
109 1 3.086984 0.98712754 0.018691593753475646 1
|
||||
111 1 1.7568789 0.87731296 0.18883651702799517 1
|
||||
112 1 1.6445342 0.8540643 0.22758342873057635 1
|
||||
113 1 0.6842432 0.51349396 0.96158080168876259 1
|
||||
115 0 0.622148 0.48581156 0.95963092284129548 1
|
||||
117 1 2.5976748 0.9697323 0.044341579615316734 1
|
||||
120 0 -1.091419 0.042565107 0.062753710448442621 0
|
||||
121 0 -0.8454566 0.06449463 0.096182163389686826 0
|
||||
122 1 1.6995935 0.8658843 0.2077538241257372 1
|
||||
123 1 0.7946054 0.5623832 0.83037466167331231 1
|
||||
125 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
128 1 2.4481912 0.9608458 0.057623132090207051 1
|
||||
129 0 -3.245754 0.00095212116 0.0013742748198001195 0
|
||||
131 0 -1.2457733 0.032655496 0.04789832154160352 0
|
||||
132 1 4.368551 0.99867576 0.0019117353514262697 1
|
||||
133 0 -0.9280808 0.05615321 0.083375404538101522 0
|
||||
137 0 -0.90333724 0.058538686 0.087026281986755216 0
|
||||
138 0 -1.242892 0.03281823 0.048141045375543319 0
|
||||
141 0 -0.7543722 0.075018466 0.11250352966763072 0
|
||||
144 0 -0.9906934 0.050519254 0.074789349927547044 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -0.9058513 0.05829204 0.086648368555505312 0
|
||||
150 0 -0.75773776 0.07460297 0.11185562463682144 0
|
||||
151 1 0.6419226 0.49462587 1.0155904075853948 1
|
||||
152 1 3.3571246 0.99201006 0.011573348195765793 1
|
||||
154 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
156 0 -0.4561 0.12131696 0.18658525021355726 0
|
||||
161 0 -1.3224158 0.028602345 0.041866091129922997 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 -0.9897763 0.050597772 4.3047823303910286 0
|
||||
169 0 -0.29987767 0.1542868 0.24175960140931579 0
|
||||
171 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
173 1 3.7876685 0.99627703 0.005381127280976423 1
|
||||
174 1 2.3288984 0.9520074 0.070955287606744438 1
|
||||
176 0 -1.2457733 0.032655496 0.04789832154160352 0
|
||||
177 1 1.9036561 0.90282637 0.14747953901372829 1
|
||||
179 1 1.2055982 0.7278852 0.4582171907293866 1
|
||||
180 0 -0.75773776 0.07460297 0.11185562463682144 0
|
||||
181 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
183 1 4.625166 0.9991617 0.0012098891845332906 1
|
||||
187 1 1.3945665 0.7893444 0.34127324708571061 1
|
||||
188 1 4.178164 0.9981413 0.0026840487014598499 1
|
||||
189 0 -0.64305043 0.090012915 0.1360820255788632 0
|
||||
191 1 2.8891368 0.9817794 0.02652920403405 1
|
||||
192 0 -1.4362687 0.023469044 0.034262316509625829 0
|
||||
196 0 1.7927873 0.88404197 3.1083253038659477 1
|
||||
198 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
199 0 -1.1409216 0.039108574 0.057554669698855809 0
|
||||
201 1 0.65003836 0.49824464 1.0050738006187003 1
|
||||
202 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
204 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
205 1 4.989299 0.99956197 0.00063208869904390983 1
|
||||
206 1 1.9608462 0.9114153 0.13381953886952919 1
|
||||
207 0 -0.75773776 0.07460297 0.11185562463682144 0
|
||||
209 0 -1.355017 0.02703023 0.039533112826534662 0
|
||||
210 1 4.607149 0.99913436 0.00124939285841888 1
|
||||
211 1 4.2001557 0.9982127 0.0025808429486298 1
|
||||
212 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
216 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
218 1 3.0803084 0.9869754 0.01891400956844376 1
|
||||
219 0 -1.7217999 0.01423632 0.020686268042626313 0
|
||||
223 1 2.0607462 0.92478627 0.11280811663527961 1
|
||||
226 1 2.976654 0.9843716 0.022725055815901302 1
|
||||
228 0 -0.75773776 0.07460297 0.11185562463682144 0
|
||||
233 1 1.1954995 0.72430277 0.46533520475439299 1
|
||||
237 1 1.5635142 0.83511573 0.2599519540341042 1
|
||||
239 1 1.5942044 0.8425159 0.24722420599110162 1
|
||||
240 0 -0.44303542 0.12382305 0.190705827926093 0
|
||||
241 0 -1.1017247 0.04182224 0.061634766659064158 0
|
||||
242 0 -1.2457733 0.032655496 0.04789832154160352 0
|
||||
244 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
246 1 4.803745 0.99939024 0.00087995956653559235 1
|
||||
247 1 2.231325 0.94340026 0.084058090624520021 1
|
||||
248 0 -0.30466565 0.15317574 0.23986549676122609 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -0.23384196 0.170289 0.26931917303375064 0
|
||||
252 0 2.674905 0.9735242 5.2391826115454805 1
|
||||
254 1 1.6914941 0.86419773 0.21056665180197529 1
|
||||
257 0 -1.1409216 0.039108574 0.057554669698855809 0
|
||||
258 0 -1.3978926 0.025090056 0.036659136211532849 0
|
||||
259 0 2.8400352 0.98014474 5.6543348753447127 1
|
||||
260 1 2.1724033 0.93751985 0.093078860596017152 1
|
||||
262 1 3.8907902 0.9969006 0.0044784066680384508 1
|
||||
267 1 1.7487614 0.875746 0.19141558207374856 1
|
||||
268 1 0.94744414 0.6279553 0.67126618787003101 1
|
||||
269 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
271 0 -1.6949314 0.014924774 0.021694193556481233 0
|
||||
272 1 1.7487614 0.875746 0.19141558207374856 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -1.1409216 0.039108574 0.057554669698855809 0
|
||||
277 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
278 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
279 1 0.754449 0.5446839 0.87650894066045415 1
|
||||
280 0 -1.3978926 0.025090056 0.036659136211532849 0
|
||||
283 1 2.063809 0.92516536 0.11221685270162182 1
|
||||
284 1 4.303939 0.9985143 0.0021450136016770457 1
|
||||
285 1 3.3729553 0.99223083 0.011252306660547107 1
|
||||
288 1 0.99726164 0.6484684 0.62489187668793977 1
|
||||
290 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
291 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
293 1 2.706591 0.97494256 0.036610864416770743 1
|
||||
296 0 1.4769651 0.8127457 2.4169291722233872 1
|
||||
297 0 ? ? ? 0
|
||||
299 1 1.2712231 0.7504423 0.41418689458258162 1
|
||||
300 1 1.7633154 0.8785433 0.18681467360632081 1
|
||||
301 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
303 0 -1.0911703 0.042583186 0.062780952499414497 0
|
||||
304 1 2.525732 0.96573 0.050308182809549816 1
|
||||
308 1 1.8170813 0.8884107 0.17070134649429849 1
|
||||
309 0 -0.64698875 0.089439176 0.13517270479220425 0
|
||||
311 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
312 1 -0.8908938 0.05977395 4.0643392645635528 0
|
||||
314 0 -0.5160317 0.11037499 0.16873074315368042 0
|
||||
316 1 2.4893982 0.9635192 0.053614656232456148 1
|
||||
317 1 3.665472 0.99537456 0.0066885795994165408 1
|
||||
319 0 -0.24271041 0.16806562 0.26545836089047964 0
|
||||
321 0 ? ? ? 0
|
||||
323 1 3.1636813 0.98875487 0.016315201631471071 1
|
||||
327 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
328 1 2.9186125 0.9826964 0.025182303623502378 1
|
||||
329 1 0.74742377 0.5415745 0.88476834178472463 1
|
||||
331 0 -1.839713 0.011567288 0.016785337816190248 0
|
||||
332 0 -0.73608303 0.07731374 0.11608792717248644 0
|
||||
333 1 2.7863374 0.97819245 0.031809767388371137 1
|
||||
336 1 3.3757868 0.99226964 0.011195889084666483 1
|
||||
338 0 -0.5160317 0.11037499 0.16873074315368042 0
|
||||
343 0 -0.6041921 0.09585385 0.14537210047268928 0
|
||||
344 1 0.9535081 0.63047874 0.66548037237672752 1
|
||||
346 0 -0.58265823 0.09923471 0.15077685530701476 0
|
||||
347 0 0.10433553 0.27281633 0.45960829325841762 1
|
||||
348 1 -0.23598832 0.16974875 2.5585271160059704 0
|
||||
349 1 1.8222505 0.8893215 0.16922302083453664 1
|
||||
350 0 -1.1943592 0.03567992 0.052416007552203513 0
|
||||
352 0 0.39703548 0.38738978 0.70695865754257714 1
|
||||
353 1 4.70303 0.9992704 0.0010530040079690082 1
|
||||
354 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
355 0 -1.4002835 0.024985954 0.036505092732930497 0
|
||||
358 1 2.1924207 0.93957895 0.089913702191056807 1
|
||||
360 1 3.1165805 0.98778135 0.017736371070864961 1
|
||||
361 1 3.5879738 0.9946925 0.0076774897651724966 1
|
||||
366 1 4.133538 0.9979876 0.0029061642527560584 1
|
||||
368 0 -0.53276235 0.10747861 0.16404135186615329 0
|
||||
370 0 -0.7993899 0.06963325 0.10412856157392694 0
|
||||
371 0 -0.53276235 0.10747861 0.16404135186615329 0
|
||||
373 0 -1.4435415 0.023173569 0.033825857207288056 0
|
||||
376 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
377 0 -0.5160317 0.11037499 0.16873074315368042 0
|
||||
378 0 -1.3080177 0.029324593 0.042939154580453193 0
|
||||
379 0 -1.3453482 0.027487509 0.040211314550606551 0
|
||||
381 1 2.7633152 0.9772991 0.03312794038683136 1
|
||||
383 0 -0.7543722 0.075018466 0.11250352966763072 0
|
||||
384 0 -0.7543722 0.075018466 0.11250352966763072 0
|
||||
387 0 -1.0723433 0.043973517 0.064877511372194857 0
|
||||
388 0 -0.7868063 0.071101464 0.10640707640839275 0
|
||||
389 0 -1.2217852 0.03403447 0.049956384125330544 0
|
||||
391 1 4.20685 0.99823385 0.0025502616936185539 1
|
||||
392 0 -1.1409216 0.039108574 0.057554669698855809 0
|
||||
395 0 -1.1409216 0.039108574 0.057554669698855809 0
|
||||
396 0 -1.3978926 0.025090056 0.036659136211532849 0
|
||||
398 0 -0.5531336 0.10404241 0.15849765208199521 0
|
||||
399 0 -0.18065979 0.18411385 0.29356023608021697 0
|
||||
404 0 -0.03948412 0.22497457 0.3676844512131936 0
|
||||
406 0 -0.86651665 0.06226492 0.092747689966697966 0
|
||||
409 0 -1.0991187 0.04200891 0.061915857449932089 0
|
||||
413 0 -1.6434052 0.016337989 0.023765408053079053 0
|
||||
414 1 2.9795613 0.9844512 0.022608439510118272 1
|
||||
415 0 1.0583365 0.67288256 1.6121194037000521 1
|
||||
416 1 1.0621965 0.6743962 0.56833165160726351 1
|
||||
418 0 -0.7626318 0.07400255 0.11091987279503104 0
|
||||
419 0 -1.0071353 0.04913092 0.072681377460547272 0
|
||||
422 0 -0.12223 0.20028716 0.32244604911339331 0
|
||||
423 0 -1.3289838 0.028278641 0.041385415007353897 0
|
||||
428 0 -0.8386307 0.065233134 0.097321497608580276 0
|
||||
429 0 -0.9906934 0.050519254 0.074789349927547044 0
|
||||
430 0 0.09331045 0.26893246 0.45192340196012404 1
|
||||
434 0 3.032602 0.9858349 6.1415150532643841 1
|
||||
436 1 1.6801375 0.8618028 0.21457028167151404 1
|
||||
439 0 -1.1979982 0.035457265 0.052082934645488306 0
|
||||
440 1 0.52904445 0.4445204 1.1696784208923632 1
|
||||
441 0 0.3223733 0.35629743 0.63553387359623403 1
|
||||
442 0 -0.018005634 0.23172477 0.38030485237500838 0
|
||||
449 1 4.412463 0.9987754 0.0017677743903420913 1
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
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||||
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|
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|
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|
||||
635 0 0.09085885 0.025926873 0.037898010594882688 1
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
655 0 -2.546677 3.4451296E-05 4.9703569358758513E-05 0
|
||||
658 1 3.4511225 0.9921988 0.011298846321539465 1
|
||||
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|
||||
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|
||||
663 0 -0.26095006 0.010844616 0.015730926985344458 0
|
||||
664 0 -2.922262 1.3365113E-05 1.9281910901894112E-05 0
|
||||
666 0 0.4115183 0.05637177 0.083709516582474261 1
|
||||
667 0 -2.7890928 1.8697447E-05 2.6974965739685937E-05 0
|
||||
669 1 3.1079195 0.98166376 0.02669913324302689 1
|
||||
671 0 -3.145483 7.6131073E-06 1.0983433956737723E-05 0
|
||||
672 0 -2.5093875 3.7847214E-05 5.4603021409950756E-05 0
|
||||
673 0 0.45997784 0.063234195 0.094239681324720354 1
|
||||
674 0 -1.9880302 0.00014087795 0.00020325823731327596 0
|
||||
675 0 0.18965189 0.033017885 0.048438888486930072 1
|
||||
676 0 -0.38223547 0.008010448 0.011603169128622314 0
|
||||
677 0 -0.17125607 0.013559076 0.01969544151547159 0
|
||||
684 0 -0.40331545 0.0075989855 0.011004884765518618 0
|
||||
686 0 -0.40331545 0.0075989855 0.011004884765518618 0
|
||||
687 0 -0.1104493 0.015770128 0.022932791041876899 0
|
||||
690 0 -0.2828443 0.010268201 0.014890463480481621 0
|
||||
695 0 -0.36452106 0.008373251 0.012130906699393275 0
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 16 rows with missing feature/label values
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 220 | 19 | 0.9205
|
||||
negative || 25 | 419 | 0.9437
|
||||
||======================
|
||||
Precision || 0.8980 | 0.9566 |
|
||||
OVERALL 0/1 ACCURACY: 0.935578
|
||||
LOG LOSS/instance: 0.315898
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.661780
|
||||
AUC: 0.963945
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.963945 (0.0000)
|
||||
Accuracy: 0.935578 (0.0000)
|
||||
Positive precision: 0.897959 (0.0000)
|
||||
Positive recall: 0.920502 (0.0000)
|
||||
Negative precision: 0.956621 (0.0000)
|
||||
Negative recall: 0.943694 (0.0000)
|
||||
Log-loss: 0.315898 (0.0000)
|
||||
Log-loss reduction: 0.661780 (0.0000)
|
||||
F1 Score: 0.909091 (0.0000)
|
||||
AUPRC: 0.951812 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LdSvm
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.963945 0.935578 0.897959 0.920502 0.956621 0.943694 0.315898 0.66178 0.909091 0.951812 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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658 1 3.2333775 0.99481493 0.0074999320648128755 1
|
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|
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|
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|
||||
668 1 0.28655383 0.40319055 1.3104662582023581 1
|
||||
669 1 3.7242916 0.9979703 0.002931238404433384 1
|
||||
670 1 3.6207821 0.99752593 0.003573750615050979 1
|
||||
671 0 -1.1575122 0.040682934 0.059920372844633439 0
|
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672 0 -1.4261553 0.024713239 0.036101621382306076 0
|
||||
673 0 -0.88826156 0.06634313 0.09903565308941864 0
|
||||
674 0 -0.9631871 0.057981633 0.08617290529710428 0
|
||||
675 0 -0.7962933 0.07813441 0.11737167709216953 0
|
||||
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|
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|
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|
||||
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|
||||
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|
||||
681 1 4.800027 0.9997414 0.00037316461789348626 1
|
||||
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|
||||
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|
||||
684 0 -0.69115174 0.09394281 0.14232598504884403 0
|
||||
685 0 -0.69115174 0.09394281 0.14232598504884403 0
|
||||
686 0 -0.69115174 0.09394281 0.14232598504884403 0
|
||||
687 0 -0.90419805 0.06447571 0.096152979196183611 0
|
||||
688 0 -1.179642 0.039059177 0.057480505774278046 0
|
||||
689 0 -1.8485187 0.011150603 0.016177281698189196 0
|
||||
690 0 -0.5903109 0.11173821 0.17094316605683935 0
|
||||
691 1 3.3737745 0.99603355 0.0057337576078232743 1
|
||||
692 0 -1.0216625 0.052153874 0.077275225159652808 0
|
||||
693 0 -1.4660513 0.022935389 0.033474126910530901 0
|
||||
694 0 -1.1688536 0.039842848 0.058657538805067903 0
|
||||
695 0 -0.85865223 0.06994668 0.10461466108155069 0
|
||||
696 1 2.3202627 0.97086823 0.04265259431918976 1
|
||||
697 1 2.0291917 0.9501872 0.073716313078114964 1
|
||||
698 1 2.0917692 0.955568 0.06556952964793962 1
|
||||
|
|
@ -0,0 +1,112 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 130 | 4 | 0.9701
|
||||
negative || 7 | 213 | 0.9682
|
||||
||======================
|
||||
Precision || 0.9489 | 0.9816 |
|
||||
OVERALL 0/1 ACCURACY: 0.968927
|
||||
LOG LOSS/instance: 0.143504
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.850048
|
||||
AUC: 0.994132
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 96 | 9 | 0.9143
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9697 | 0.9609 |
|
||||
OVERALL 0/1 ACCURACY: 0.963526
|
||||
LOG LOSS/instance: 0.111794
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.876259
|
||||
AUC: 0.997236
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995684 (0.0016)
|
||||
Accuracy: 0.966226 (0.0027)
|
||||
Positive precision: 0.959301 (0.0104)
|
||||
Positive recall: 0.942217 (0.0279)
|
||||
Negative precision: 0.971218 (0.0103)
|
||||
Negative recall: 0.977394 (0.0092)
|
||||
Log-loss: 0.127649 (0.0159)
|
||||
Log-loss reduction: 0.863153 (0.0131)
|
||||
F1 Score: 0.950293 (0.0091)
|
||||
AUPRC: 0.991584 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387
|
||||
[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725
|
||||
[2] (%Time%) 3 iterations Loss: 0.5179428458213806 Improvement: 0.09906
|
||||
[2] (%Time%) 4 iterations Loss: 0.4057178199291229 Improvement: 0.109
|
||||
[2] (%Time%) 5 iterations Loss: 0.3352813422679901 Improvement: 0.08004
|
||||
[2] (%Time%) 6 iterations Loss: 0.3197685182094574 Improvement: 0.03163
|
||||
[2] (%Time%) 7 iterations Loss: 0.31049972772598267 Improvement: 0.01486
|
||||
[2] (%Time%) 8 iterations Loss: 0.2985224425792694 Improvement: 0.0127
|
||||
[2] (%Time%) 9 iterations Loss: 0.2738857865333557 Improvement: 0.02165
|
||||
[2] (%Time%) 10 iterations Loss: 0.2316397875547409 Improvement: 0.0371
|
||||
[2] (%Time%) 11 iterations Loss: 0.17319267988204956 Improvement: 0.05311
|
||||
[2] (%Time%) 12 iterations Loss: 0.13885831832885742 Improvement: 0.03903
|
||||
[2] (%Time%) 13 iterations Loss: 0.11185470223426819 Improvement: 0.03001
|
||||
[2] (%Time%) 14 iterations Loss: 0.10666187852621078 Improvement: 0.0114
|
||||
[2] (%Time%) 15 iterations Loss: 0.09659970551729202 Improvement: 0.0104
|
||||
[2] (%Time%) 16 iterations Loss: 0.08454433083534241 Improvement: 0.01164
|
||||
[2] (%Time%) 17 iterations Loss: 0.08044131845235825 Improvement: 0.005987
|
||||
[2] (%Time%) 18 iterations Loss: 0.07466444373130798 Improvement: 0.00583
|
||||
[2] (%Time%) 19 iterations Loss: 0.07378975301980972 Improvement: 0.002113
|
||||
[2] (%Time%) 20 iterations Loss: 0.07328920811414719 Improvement: 0.0009038
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'LBFGS data prep #2' started.
|
||||
[3] 'LBFGS data prep #2' finished in %Time%.
|
||||
[4] 'LBFGS Optimizer #2' started.
|
||||
[4] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603
|
||||
[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156
|
||||
[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083
|
||||
[4] (%Time%) 4 iterations Loss: 0.40101736783981323 Improvement: 0.1627
|
||||
[4] (%Time%) 5 iterations Loss: 0.36694765090942383 Improvement: 0.06612
|
||||
[4] (%Time%) 6 iterations Loss: 0.3179488480091095 Improvement: 0.05328
|
||||
[4] (%Time%) 7 iterations Loss: 0.2625056505203247 Improvement: 0.0549
|
||||
[4] (%Time%) 8 iterations Loss: 0.21261096000671387 Improvement: 0.05115
|
||||
[4] (%Time%) 9 iterations Loss: 0.17287731170654297 Improvement: 0.04259
|
||||
[4] (%Time%) 10 iterations Loss: 0.14646978676319122 Improvement: 0.03045
|
||||
[4] (%Time%) 11 iterations Loss: 0.11033976078033447 Improvement: 0.03471
|
||||
[4] (%Time%) 12 iterations Loss: 0.10183008015155792 Improvement: 0.01506
|
||||
[4] (%Time%) 13 iterations Loss: 0.09570615738630295 Improvement: 0.008358
|
||||
[4] (%Time%) 14 iterations Loss: 0.09520583599805832 Improvement: 0.002465
|
||||
[4] (%Time%) 15 iterations Loss: 0.08928800374269485 Improvement: 0.005055
|
||||
[4] (%Time%) 16 iterations Loss: 0.08804423362016678 Improvement: 0.002196
|
||||
[4] (%Time%) 17 iterations Loss: 0.0877317488193512 Improvement: 0.0007835
|
||||
[4] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995684 0.966226 0.959301 0.942217 0.971218 0.977394 0.127649 0.863153 0.950293 0.991584 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 11.843378 0.9999928 1.0404987898474284E-05 1
|
||||
6 0 -0.59487915 0.35551614 0.63378385604457876 0
|
||||
8 0 -4.651406 0.009457864 0.013709748448272217 0
|
||||
9 0 -4.4850006 0.01115113 0.016178050759675634 0
|
||||
10 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
11 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
18 1 7.386753 0.999381 0.00089329641580267009 1
|
||||
20 1 8.050364 0.9996811 0.00046012695871573505 1
|
||||
21 1 7.1970863 0.9992518 0.001079853140832915 1
|
||||
25 1 1.1823997 0.765379 0.38575375519663319 1
|
||||
28 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
31 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
32 1 7.011631 0.9990995 0.0012997422459666663 1
|
||||
35 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
37 0 -0.62716675 0.34815323 0.61739523354932679 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 3.0365572 0.9541986 0.067638527006529184 1
|
||||
44 1 6.6134357 0.9986596 0.0019350700897898159 1
|
||||
45 0 -5.56501 0.0038149324 0.0055143091492393511 0
|
||||
46 1 3.9145546 0.98044074 0.028497667659147116 1
|
||||
48 0 -3.536106 0.028302178 0.041420359423840737 0
|
||||
50 1 1.0294714 0.7368134 0.44062874481429237 1
|
||||
51 1 -0.36414146 0.4099574 1.2864540611401696 0
|
||||
52 1 3.4916897 0.9704504 0.043273614682259293 1
|
||||
54 1 7.767933 0.9995771 0.00061023749621146994 1
|
||||
56 1 3.6949062 0.9757528 0.035412440314029049 1
|
||||
60 1 0.8933983 0.709591 0.49494044136238835 1
|
||||
63 1 -0.91438866 0.28610262 1.8053953717549116 0
|
||||
64 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
66 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
68 1 9.950766 0.9999523 6.8794700551634382E-05 1
|
||||
69 0 -5.1962605 0.0055067404 0.0079665020079430701 0
|
||||
70 0 -4.2990217 0.013399845 0.019462580884246464 0
|
||||
71 1 9.312965 0.99990976 0.00013019674136170853 1
|
||||
72 0 -1.8089752 0.14076203 0.21887034881184167 0
|
||||
73 1 7.979315 0.99965763 0.00049401874647617746 1
|
||||
74 1 2.2298784 0.9029007 0.14736077113903101 1
|
||||
76 0 -5.8814692 0.0027829157 0.0040204956554648792 0
|
||||
77 0 -3.5381575 0.028245818 0.041336683395315918 0
|
||||
79 0 -5.4287415 0.00436944 0.0063175816728364229 0
|
||||
82 0 -3.449263 0.030790843 0.045120059685660907 0
|
||||
88 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
90 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
91 0 -5.493143 0.0040980275 0.0059243513065432981 0
|
||||
92 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
93 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
95 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
96 0 -5.894248 0.002747677 0.0039695160053964361 0
|
||||
97 0 -3.708334 0.023931576 0.034945808171670999 0
|
||||
98 1 6.953968 0.9990461 0.0013768619797486616 1
|
||||
99 1 10.508131 0.9999727 3.9384564628212499E-05 1
|
||||
100 1 3.3712034 0.96679235 0.048722044835642818 1
|
||||
102 0 -4.0874667 0.016504716 0.024009960483151255 0
|
||||
104 1 11.012169 0.9999835 2.3819793783233171E-05 1
|
||||
105 1 2.1146507 0.892319 0.16436849741706283 1
|
||||
106 1 8.185759 0.99972147 0.00040189343653238463 1
|
||||
108 0 -5.7144 0.0032872874 0.0047503654649493484 0
|
||||
109 1 7.084921 0.99916303 0.0012079957894204731 1
|
||||
111 1 3.9758768 0.9815827 0.026818270809047463 1
|
||||
112 1 9.693426 0.9999383 8.900376720685931E-05 1
|
||||
113 1 9.182865 0.99989724 0.00014825666248348076 1
|
||||
115 0 -4.4851866 0.0111490805 0.016175060118282753 0
|
||||
117 1 10.228838 0.9999639 5.2111684364922767E-05 1
|
||||
120 0 -5.004115 0.006665549 0.00964854643436522 0
|
||||
121 0 -3.32616 0.034684572 0.050927658701288833 0
|
||||
122 1 9.03846 0.99988127 0.00017130489018547468 1
|
||||
123 1 3.8230705 0.97860706 0.031198406166673034 1
|
||||
125 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
128 1 3.7270908 0.97650266 0.034304125384504093 1
|
||||
129 0 -5.6416936 0.0035343207 0.0051079789517955157 0
|
||||
131 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
132 1 8.127455 0.9997048 0.00042597791694096366 1
|
||||
133 0 -5.028683 0.0065048365 0.0094151506639061164 0
|
||||
137 0 -5.7798095 0.0030797895 0.004450053114006914 0
|
||||
138 0 -4.7220993 0.008818033 0.012778154961972425 0
|
||||
141 0 -6.1809144 0.0020642655 0.0029811836999283146 0
|
||||
144 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -5.3056383 0.004939014 0.0071431457429921262 0
|
||||
150 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
151 1 4.990402 0.99324304 0.0097813179599174817 1
|
||||
152 1 8.769493 0.9998446 0.0002241968047441011 1
|
||||
154 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
156 0 -5.666343 0.0034485627 0.0049838227609212849 0
|
||||
161 0 -3.9929457 0.018111233 0.026368495915666784 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 6.799999 0.9988875 0.0016059215653949079 1
|
||||
169 0 -6.412894 0.001637585 0.0023644722930159967 0
|
||||
171 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
173 1 15.010418 0.9999997 4.2995669122556443E-07 1
|
||||
174 1 6.477541 0.99846476 0.0022165804928464235 1
|
||||
176 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
177 1 3.1821003 0.96015507 0.058660667798257342 1
|
||||
179 1 2.6151247 0.9318287 0.10186336384757579 1
|
||||
180 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
181 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
183 1 8.690121 0.9998318 0.00024268793151739886 1
|
||||
187 1 13.8902645 0.99999905 1.3758618629646341E-06 1
|
||||
188 1 9.6294 0.99993426 9.4851549941511306E-05 1
|
||||
189 0 -5.009738 0.0066284225 0.0095946261803761847 0
|
||||
191 1 9.013945 0.9998783 0.00017560497342409987 1
|
||||
192 0 -4.3429666 0.012831134 0.018631200068976669 0
|
||||
196 0 6.601473 0.99864346 9.5258504515026932 1
|
||||
198 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
199 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
201 1 9.95586 0.99995255 6.8450718888590702E-05 1
|
||||
202 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
204 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
205 1 11.418911 0.99998903 1.5822490644178947E-05 1
|
||||
206 1 4.5284576 0.989318 0.015493749528120913 1
|
||||
207 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
209 0 -3.8258748 0.021334283 0.031111932565010495 0
|
||||
210 1 13.808363 0.999999 1.4618532729665815E-06 1
|
||||
211 1 9.1291275 0.9998916 0.00015642670107082299 1
|
||||
212 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
216 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
218 1 7.523016 0.9994598 0.00077954995658007442 1
|
||||
219 0 -3.2632842 0.03685246 0.054171280815609818 0
|
||||
223 1 5.488367 0.99588245 0.0059526303015457567 1
|
||||
226 1 9.383381 0.9999159 0.00012133886268507978 1
|
||||
228 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
233 1 6.0140915 0.9975619 0.0035217701767182837 1
|
||||
237 1 4.8346376 0.9921131 0.011423479473879502 1
|
||||
239 1 6.125063 0.9978174 0.0031522716860362876 1
|
||||
240 0 -2.9684925 0.048869748 0.072285170704597557 0
|
||||
241 0 -4.0547514 0.017044246 0.02480161749270815 0
|
||||
242 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
244 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
246 1 10.455524 0.9999712 4.1534408054175585E-05 1
|
||||
247 1 2.7647648 0.94074184 0.088129229047667901 1
|
||||
248 0 -3.5269709 0.028554494 0.041795025268993979 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -6.300976 0.0018311545 0.0026442191783606385 0
|
||||
252 0 4.4676695 0.98865616 6.4619475094968903 1
|
||||
254 1 5.2682514 0.9948738 0.0074145323465134721 1
|
||||
257 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
258 0 -4.5105443 0.0108729545 0.015772259304918778 0
|
||||
259 0 2.3494902 0.9128937 3.5210793310198607 1
|
||||
260 1 8.936718 0.9998686 0.0001896233337798923 1
|
||||
262 1 8.7687435 0.9998445 0.00022436881413363666 1
|
||||
267 1 3.7561016 0.9771592 0.033334464763958606 1
|
||||
268 1 8.790094 0.99984777 0.00021963856339410703 1
|
||||
269 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
271 0 -3.708334 0.023931576 0.034945808171670999 0
|
||||
272 1 3.7561016 0.9771592 0.033334464763958606 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
277 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
278 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
279 1 6.2110634 0.9979969 0.0028927226189131923 1
|
||||
280 0 -4.5105443 0.0108729545 0.015772259304918778 0
|
||||
283 1 5.404396 0.99552333 0.0064729639725847315 1
|
||||
284 1 6.454686 0.99842936 0.002267738785887697 1
|
||||
285 1 13.933638 0.9999991 1.2898704580881792E-06 1
|
||||
288 1 1.3863392 0.80000716 0.32191517474857001 1
|
||||
290 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
291 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
293 1 4.889885 0.99253386 0.0108117709104356 1
|
||||
296 0 1.1145687 0.7529799 2.0172995080723655 1
|
||||
297 0 ? ? ? 0
|
||||
299 1 7.924219 0.99963826 0.00052197576964742038 1
|
||||
300 1 5.0112934 0.9933818 0.0095797823682855481 1
|
||||
301 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
303 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
304 1 5.440776 0.9956826 0.0062421803871212491 1
|
||||
308 1 6.8917904 0.99898493 0.0014651760239549505 1
|
||||
309 0 -1.1444454 0.24150512 0.39878864569561184 0
|
||||
311 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
312 1 2.842554 0.9449325 0.08171678774977649 1
|
||||
314 0 -6.0649276 0.0023175427 0.0033473876824224357 0
|
||||
316 1 3.640625 0.9744348 0.037362448110842938 1
|
||||
317 1 8.8895035 0.9998622 0.00019882564438936136 1
|
||||
319 0 2.9322062 0.94941574 4.3051677453390669 1
|
||||
321 0 ? ? ? 0
|
||||
323 1 5.111249 0.9940076 0.0086712307155108279 1
|
||||
327 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
328 1 3.2484636 0.9626179 0.054964882989445796 1
|
||||
329 1 7.6532955 0.9995257 0.00068439528478836701 1
|
||||
331 0 -3.3259563 0.03469139 0.050937847383966318 0
|
||||
332 0 -3.658763 0.025117235 0.036699357968660369 0
|
||||
333 1 4.4326515 0.98825663 0.017042361290226671 1
|
||||
336 1 5.126048 0.9940951 0.0085442400249042122 1
|
||||
338 0 -6.0649276 0.0023175427 0.0033473876824224357 0
|
||||
343 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
344 1 11.158543 0.99998575 2.0552073165836138E-05 1
|
||||
346 0 -4.059361 0.01696719 0.024688526322893277 0
|
||||
347 0 -6.466573 0.0015521308 0.0022409910053584297 0
|
||||
348 1 -0.50180435 0.37711674 1.4069169035216322 0
|
||||
349 1 4.2482414 0.98591197 0.020469264505371183 1
|
||||
350 0 -4.4042664 0.012077425 0.01753011438501665 0
|
||||
352 0 -0.21888638 0.44549584 0.85072982118988294 0
|
||||
353 1 7.4398212 0.99941295 0.00084717725458209527 1
|
||||
354 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
355 0 -4.5100374 0.010878407 0.015780212665796697 0
|
||||
358 1 5.213545 0.9945871 0.0078303417437282485 1
|
||||
360 1 15.123365 0.9999997 4.2995669122556443E-07 1
|
||||
361 1 8.049475 0.9996808 0.000460557052557391 1
|
||||
366 1 13.10652 0.999998 2.9237080272005804E-06 1
|
||||
368 0 -6.5380745 0.001445181 0.0020864634396453026 0
|
||||
370 0 -4.086416 0.016521784 0.024034996995933994 0
|
||||
371 0 -6.5380745 0.001445181 0.0020864634396453026 0
|
||||
373 0 -4.0563 0.017018318 0.024763563151927057 0
|
||||
376 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
377 0 -6.0649276 0.0023175427 0.0033473876824224357 0
|
||||
378 0 -4.0511427 0.017104812 0.024890513282029784 0
|
||||
379 0 -2.889192 0.052690428 0.07809213237639738 0
|
||||
381 1 10.523899 0.9999731 3.8782609043028618E-05 1
|
||||
383 0 -6.1809144 0.0020642655 0.0029811836999283146 0
|
||||
384 0 -6.1809144 0.0020642655 0.0029811836999283146 0
|
||||
387 0 -2.7420387 0.060537852 0.090093060430203029 0
|
||||
388 0 -5.429788 0.0043648886 0.0063109866273234885 0
|
||||
389 0 -3.584724 0.026995357 0.039481405774790708 0
|
||||
391 1 9.408421 0.999918 0.00011832891629162384 1
|
||||
392 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
395 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
396 0 -4.5105443 0.0108729545 0.015772259304918778 0
|
||||
398 0 -4.7696123 0.008412302 0.012187721285820742 0
|
||||
399 0 -6.279003 0.001871759 0.0027029077486477797 0
|
||||
404 0 -5.6331215 0.003564639 0.0051518747842996225 0
|
||||
406 0 -4.463028 0.011396039 0.01653540841419877 0
|
||||
409 0 -5.123204 0.00592163 0.0085685013683719155 0
|
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
636 1 7.6272306 0.9995132 0.00070246214484526929 1
|
||||
637 0 -2.8129272 0.0566296 0.084103757705134702 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
664 0 -4.654005 0.009433544 0.013674328073502421 0
|
||||
666 0 -3.56778 0.027444001 0.040146773591305633 0
|
||||
667 0 -4.8003316 0.008159887 0.011820521491909639 0
|
||||
669 1 5.618209 0.996382 0.0052291386850684043 1
|
||||
671 0 -4.4956727 0.011034064 0.016007265311309419 0
|
||||
672 0 -5.3662224 0.0046500224 0.0067242102201281783 0
|
||||
673 0 -3.89156 0.020005101 0.029153855551589412 0
|
||||
674 0 -6.2625513 0.0019027485 0.0027477007196007783 0
|
||||
675 0 -3.9957395 0.018061617 0.026295597731371682 0
|
||||
676 0 -5.962692 0.0025663734 0.0037072533725928405 0
|
||||
677 0 -4.329426 0.013003784 0.018883541617014079 0
|
||||
684 0 -6.592989 0.001368065 0.0019750519213218879 0
|
||||
686 0 -6.592989 0.001368065 0.0019750519213218879 0
|
||||
687 0 -4.742455 0.008641886 0.012521789694841542 0
|
||||
690 0 -6.1162243 0.0022019132 0.0031801917320965199 0
|
||||
695 0 -6.027098 0.0024066784 0.0034762878193150487 0
|
||||
|
|
@ -0,0 +1,112 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=%Output% data=%Data% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 118 | 16 | 0.8806
|
||||
negative || 3 | 217 | 0.9864
|
||||
||======================
|
||||
Precision || 0.9752 | 0.9313 |
|
||||
OVERALL 0/1 ACCURACY: 0.946328
|
||||
LOG LOSS/instance: 0.143504
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.850048
|
||||
AUC: 0.994132
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 81 | 24 | 0.7714
|
||||
negative || 0 | 224 | 1.0000
|
||||
||======================
|
||||
Precision || 1.0000 | 0.9032 |
|
||||
OVERALL 0/1 ACCURACY: 0.927052
|
||||
LOG LOSS/instance: 0.111794
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.876259
|
||||
AUC: 0.997236
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995684 (0.0016)
|
||||
Accuracy: 0.936690 (0.0096)
|
||||
Positive precision: 0.987603 (0.0124)
|
||||
Positive recall: 0.826013 (0.0546)
|
||||
Negative precision: 0.917278 (0.0141)
|
||||
Negative recall: 0.993182 (0.0068)
|
||||
Log-loss: 0.127649 (0.0159)
|
||||
Log-loss reduction: 0.863153 (0.0131)
|
||||
F1 Score: 0.898229 (0.0273)
|
||||
AUPRC: 0.991584 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387
|
||||
[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725
|
||||
[2] (%Time%) 3 iterations Loss: 0.5179428458213806 Improvement: 0.09906
|
||||
[2] (%Time%) 4 iterations Loss: 0.4057178199291229 Improvement: 0.109
|
||||
[2] (%Time%) 5 iterations Loss: 0.3352813422679901 Improvement: 0.08004
|
||||
[2] (%Time%) 6 iterations Loss: 0.3197685182094574 Improvement: 0.03163
|
||||
[2] (%Time%) 7 iterations Loss: 0.31049972772598267 Improvement: 0.01486
|
||||
[2] (%Time%) 8 iterations Loss: 0.2985224425792694 Improvement: 0.0127
|
||||
[2] (%Time%) 9 iterations Loss: 0.2738857865333557 Improvement: 0.02165
|
||||
[2] (%Time%) 10 iterations Loss: 0.2316397875547409 Improvement: 0.0371
|
||||
[2] (%Time%) 11 iterations Loss: 0.17319267988204956 Improvement: 0.05311
|
||||
[2] (%Time%) 12 iterations Loss: 0.13885831832885742 Improvement: 0.03903
|
||||
[2] (%Time%) 13 iterations Loss: 0.11185470223426819 Improvement: 0.03001
|
||||
[2] (%Time%) 14 iterations Loss: 0.10666187852621078 Improvement: 0.0114
|
||||
[2] (%Time%) 15 iterations Loss: 0.09659970551729202 Improvement: 0.0104
|
||||
[2] (%Time%) 16 iterations Loss: 0.08454433083534241 Improvement: 0.01164
|
||||
[2] (%Time%) 17 iterations Loss: 0.08044131845235825 Improvement: 0.005987
|
||||
[2] (%Time%) 18 iterations Loss: 0.07466444373130798 Improvement: 0.00583
|
||||
[2] (%Time%) 19 iterations Loss: 0.07378975301980972 Improvement: 0.002113
|
||||
[2] (%Time%) 20 iterations Loss: 0.07328920811414719 Improvement: 0.0009038
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'LBFGS data prep #2' started.
|
||||
[3] 'LBFGS data prep #2' finished in %Time%.
|
||||
[4] 'LBFGS Optimizer #2' started.
|
||||
[4] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603
|
||||
[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156
|
||||
[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083
|
||||
[4] (%Time%) 4 iterations Loss: 0.40101736783981323 Improvement: 0.1627
|
||||
[4] (%Time%) 5 iterations Loss: 0.36694765090942383 Improvement: 0.06612
|
||||
[4] (%Time%) 6 iterations Loss: 0.3179488480091095 Improvement: 0.05328
|
||||
[4] (%Time%) 7 iterations Loss: 0.2625056505203247 Improvement: 0.0549
|
||||
[4] (%Time%) 8 iterations Loss: 0.21261096000671387 Improvement: 0.05115
|
||||
[4] (%Time%) 9 iterations Loss: 0.17287731170654297 Improvement: 0.04259
|
||||
[4] (%Time%) 10 iterations Loss: 0.14646978676319122 Improvement: 0.03045
|
||||
[4] (%Time%) 11 iterations Loss: 0.11033976078033447 Improvement: 0.03471
|
||||
[4] (%Time%) 12 iterations Loss: 0.10183008015155792 Improvement: 0.01506
|
||||
[4] (%Time%) 13 iterations Loss: 0.09570615738630295 Improvement: 0.008358
|
||||
[4] (%Time%) 14 iterations Loss: 0.09520583599805832 Improvement: 0.002465
|
||||
[4] (%Time%) 15 iterations Loss: 0.08928800374269485 Improvement: 0.005055
|
||||
[4] (%Time%) 16 iterations Loss: 0.08804423362016678 Improvement: 0.002196
|
||||
[4] (%Time%) 17 iterations Loss: 0.0877317488193512 Improvement: 0.0007835
|
||||
[4] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995684 0.93669 0.987603 0.826013 0.917278 0.993182 0.127649 0.863153 0.898229 0.991584 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 11.843378 0.9999928 1.0404987898474284E-05 1
|
||||
6 0 -0.59487915 0.35551614 0.63378385604457876 0
|
||||
8 0 -4.651406 0.009457864 0.013709748448272217 0
|
||||
9 0 -4.4850006 0.01115113 0.016178050759675634 0
|
||||
10 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
11 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
18 1 7.386753 0.999381 0.00089329641580267009 1
|
||||
20 1 8.050364 0.9996811 0.00046012695871573505 1
|
||||
21 1 7.1970863 0.9992518 0.001079853140832915 1
|
||||
25 1 1.1823997 0.765379 0.38575375519663319 0
|
||||
28 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
31 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
32 1 7.011631 0.9990995 0.0012997422459666663 1
|
||||
35 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
37 0 -0.62716675 0.34815323 0.61739523354932679 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 3.0365572 0.9541986 0.067638527006529184 1
|
||||
44 1 6.6134357 0.9986596 0.0019350700897898159 1
|
||||
45 0 -5.56501 0.0038149324 0.0055143091492393511 0
|
||||
46 1 3.9145546 0.98044074 0.028497667659147116 1
|
||||
48 0 -3.536106 0.028302178 0.041420359423840737 0
|
||||
50 1 1.0294714 0.7368134 0.44062874481429237 0
|
||||
51 1 -0.36414146 0.4099574 1.2864540611401696 0
|
||||
52 1 3.4916897 0.9704504 0.043273614682259293 1
|
||||
54 1 7.767933 0.9995771 0.00061023749621146994 1
|
||||
56 1 3.6949062 0.9757528 0.035412440314029049 1
|
||||
60 1 0.8933983 0.709591 0.49494044136238835 0
|
||||
63 1 -0.91438866 0.28610262 1.8053953717549116 0
|
||||
64 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
66 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
68 1 9.950766 0.9999523 6.8794700551634382E-05 1
|
||||
69 0 -5.1962605 0.0055067404 0.0079665020079430701 0
|
||||
70 0 -4.2990217 0.013399845 0.019462580884246464 0
|
||||
71 1 9.312965 0.99990976 0.00013019674136170853 1
|
||||
72 0 -1.8089752 0.14076203 0.21887034881184167 0
|
||||
73 1 7.979315 0.99965763 0.00049401874647617746 1
|
||||
74 1 2.2298784 0.9029007 0.14736077113903101 0
|
||||
76 0 -5.8814692 0.0027829157 0.0040204956554648792 0
|
||||
77 0 -3.5381575 0.028245818 0.041336683395315918 0
|
||||
79 0 -5.4287415 0.00436944 0.0063175816728364229 0
|
||||
82 0 -3.449263 0.030790843 0.045120059685660907 0
|
||||
88 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
90 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
91 0 -5.493143 0.0040980275 0.0059243513065432981 0
|
||||
92 0 -4.1094394 0.016151812 0.023492376082898361 0
|
||||
93 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
95 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
96 0 -5.894248 0.002747677 0.0039695160053964361 0
|
||||
97 0 -3.708334 0.023931576 0.034945808171670999 0
|
||||
98 1 6.953968 0.9990461 0.0013768619797486616 1
|
||||
99 1 10.508131 0.9999727 3.9384564628212499E-05 1
|
||||
100 1 3.3712034 0.96679235 0.048722044835642818 1
|
||||
102 0 -4.0874667 0.016504716 0.024009960483151255 0
|
||||
104 1 11.012169 0.9999835 2.3819793783233171E-05 1
|
||||
105 1 2.1146507 0.892319 0.16436849741706283 0
|
||||
106 1 8.185759 0.99972147 0.00040189343653238463 1
|
||||
108 0 -5.7144 0.0032872874 0.0047503654649493484 0
|
||||
109 1 7.084921 0.99916303 0.0012079957894204731 1
|
||||
111 1 3.9758768 0.9815827 0.026818270809047463 1
|
||||
112 1 9.693426 0.9999383 8.900376720685931E-05 1
|
||||
113 1 9.182865 0.99989724 0.00014825666248348076 1
|
||||
115 0 -4.4851866 0.0111490805 0.016175060118282753 0
|
||||
117 1 10.228838 0.9999639 5.2111684364922767E-05 1
|
||||
120 0 -5.004115 0.006665549 0.00964854643436522 0
|
||||
121 0 -3.32616 0.034684572 0.050927658701288833 0
|
||||
122 1 9.03846 0.99988127 0.00017130489018547468 1
|
||||
123 1 3.8230705 0.97860706 0.031198406166673034 1
|
||||
125 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
128 1 3.7270908 0.97650266 0.034304125384504093 1
|
||||
129 0 -5.6416936 0.0035343207 0.0051079789517955157 0
|
||||
131 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
132 1 8.127455 0.9997048 0.00042597791694096366 1
|
||||
133 0 -5.028683 0.0065048365 0.0094151506639061164 0
|
||||
137 0 -5.7798095 0.0030797895 0.004450053114006914 0
|
||||
138 0 -4.7220993 0.008818033 0.012778154961972425 0
|
||||
141 0 -6.1809144 0.0020642655 0.0029811836999283146 0
|
||||
144 0 -5.546282 0.0038867726 0.005618353203892556 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -5.3056383 0.004939014 0.0071431457429921262 0
|
||||
150 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
151 1 4.990402 0.99324304 0.0097813179599174817 1
|
||||
152 1 8.769493 0.9998446 0.0002241968047441011 1
|
||||
154 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
156 0 -5.666343 0.0034485627 0.0049838227609212849 0
|
||||
161 0 -3.9929457 0.018111233 0.026368495915666784 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 6.799999 0.9988875 0.0016059215653949079 1
|
||||
169 0 -6.412894 0.001637585 0.0023644722930159967 0
|
||||
171 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
173 1 15.010418 0.9999997 4.2995669122556443E-07 1
|
||||
174 1 6.477541 0.99846476 0.0022165804928464235 1
|
||||
176 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
177 1 3.1821003 0.96015507 0.058660667798257342 1
|
||||
179 1 2.6151247 0.9318287 0.10186336384757579 0
|
||||
180 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
181 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
183 1 8.690121 0.9998318 0.00024268793151739886 1
|
||||
187 1 13.8902645 0.99999905 1.3758618629646341E-06 1
|
||||
188 1 9.6294 0.99993426 9.4851549941511306E-05 1
|
||||
189 0 -5.009738 0.0066284225 0.0095946261803761847 0
|
||||
191 1 9.013945 0.9998783 0.00017560497342409987 1
|
||||
192 0 -4.3429666 0.012831134 0.018631200068976669 0
|
||||
196 0 6.601473 0.99864346 9.5258504515026932 1
|
||||
198 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
199 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
201 1 9.95586 0.99995255 6.8450718888590702E-05 1
|
||||
202 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
204 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
205 1 11.418911 0.99998903 1.5822490644178947E-05 1
|
||||
206 1 4.5284576 0.989318 0.015493749528120913 1
|
||||
207 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
209 0 -3.8258748 0.021334283 0.031111932565010495 0
|
||||
210 1 13.808363 0.999999 1.4618532729665815E-06 1
|
||||
211 1 9.1291275 0.9998916 0.00015642670107082299 1
|
||||
212 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
216 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
218 1 7.523016 0.9994598 0.00077954995658007442 1
|
||||
219 0 -3.2632842 0.03685246 0.054171280815609818 0
|
||||
223 1 5.488367 0.99588245 0.0059526303015457567 1
|
||||
226 1 9.383381 0.9999159 0.00012133886268507978 1
|
||||
228 0 -5.430295 0.0043626865 0.0063077957265197963 0
|
||||
233 1 6.0140915 0.9975619 0.0035217701767182837 1
|
||||
237 1 4.8346376 0.9921131 0.011423479473879502 1
|
||||
239 1 6.125063 0.9978174 0.0031522716860362876 1
|
||||
240 0 -2.9684925 0.048869748 0.072285170704597557 0
|
||||
241 0 -4.0547514 0.017044246 0.02480161749270815 0
|
||||
242 0 -4.911649 0.007306561 0.010579837492727742 0
|
||||
244 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
246 1 10.455524 0.9999712 4.1534408054175585E-05 1
|
||||
247 1 2.7647648 0.94074184 0.088129229047667901 0
|
||||
248 0 -3.5269709 0.028554494 0.041795025268993979 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -6.300976 0.0018311545 0.0026442191783606385 0
|
||||
252 0 4.4676695 0.98865616 6.4619475094968903 1
|
||||
254 1 5.2682514 0.9948738 0.0074145323465134721 1
|
||||
257 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
258 0 -4.5105443 0.0108729545 0.015772259304918778 0
|
||||
259 0 2.3494902 0.9128937 3.5210793310198607 0
|
||||
260 1 8.936718 0.9998686 0.0001896233337798923 1
|
||||
262 1 8.7687435 0.9998445 0.00022436881413363666 1
|
||||
267 1 3.7561016 0.9771592 0.033334464763958606 1
|
||||
268 1 8.790094 0.99984777 0.00021963856339410703 1
|
||||
269 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
271 0 -3.708334 0.023931576 0.034945808171670999 0
|
||||
272 1 3.7561016 0.9771592 0.033334464763958606 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -5.145177 0.005793681 0.0083828219211117272 0
|
||||
277 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
278 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
279 1 6.2110634 0.9979969 0.0028927226189131923 1
|
||||
280 0 -4.5105443 0.0108729545 0.015772259304918778 0
|
||||
283 1 5.404396 0.99552333 0.0064729639725847315 1
|
||||
284 1 6.454686 0.99842936 0.002267738785887697 1
|
||||
285 1 13.933638 0.9999991 1.2898704580881792E-06 1
|
||||
288 1 1.3863392 0.80000716 0.32191517474857001 0
|
||||
290 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
291 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
293 1 4.889885 0.99253386 0.0108117709104356 1
|
||||
296 0 1.1145687 0.7529799 2.0172995080723655 0
|
||||
297 0 ? ? ? 0
|
||||
299 1 7.924219 0.99963826 0.00052197576964742038 1
|
||||
300 1 5.0112934 0.9933818 0.0095797823682855481 1
|
||||
301 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
303 0 -5.312754 0.0049041654 0.0070926210434774928 0
|
||||
304 1 5.440776 0.9956826 0.0062421803871212491 1
|
||||
308 1 6.8917904 0.99898493 0.0014651760239549505 1
|
||||
309 0 -1.1444454 0.24150512 0.39878864569561184 0
|
||||
311 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
312 1 2.842554 0.9449325 0.08171678774977649 0
|
||||
314 0 -6.0649276 0.0023175427 0.0033473876824224357 0
|
||||
316 1 3.640625 0.9744348 0.037362448110842938 1
|
||||
317 1 8.8895035 0.9998622 0.00019882564438936136 1
|
||||
319 0 2.9322062 0.94941574 4.3051677453390669 0
|
||||
321 0 ? ? ? 0
|
||||
323 1 5.111249 0.9940076 0.0086712307155108279 1
|
||||
327 0 -5.947387 0.0026058496 0.0037643531610362002 0
|
||||
328 1 3.2484636 0.9626179 0.054964882989445796 1
|
||||
329 1 7.6532955 0.9995257 0.00068439528478836701 1
|
||||
331 0 -3.3259563 0.03469139 0.050937847383966318 0
|
||||
332 0 -3.658763 0.025117235 0.036699357968660369 0
|
||||
333 1 4.4326515 0.98825663 0.017042361290226671 1
|
||||
336 1 5.126048 0.9940951 0.0085442400249042122 1
|
||||
338 0 -6.0649276 0.0023175427 0.0033473876824224357 0
|
||||
343 0 -6.58202 0.0013831332 0.0019968207086486669 0
|
||||
344 1 11.158543 0.99998575 2.0552073165836138E-05 1
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||||
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|
||||
672 0 -5.3662224 0.0046500224 0.0067242102201281783 0
|
||||
673 0 -3.89156 0.020005101 0.029153855551589412 0
|
||||
674 0 -6.2625513 0.0019027485 0.0027477007196007783 0
|
||||
675 0 -3.9957395 0.018061617 0.026295597731371682 0
|
||||
676 0 -5.962692 0.0025663734 0.0037072533725928405 0
|
||||
677 0 -4.329426 0.013003784 0.018883541617014079 0
|
||||
684 0 -6.592989 0.001368065 0.0019750519213218879 0
|
||||
686 0 -6.592989 0.001368065 0.0019750519213218879 0
|
||||
687 0 -4.742455 0.008641886 0.012521789694841542 0
|
||||
690 0 -6.1162243 0.0022019132 0.0031801917320965199 0
|
||||
695 0 -6.027098 0.0024066784 0.0034762878193150487 0
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=MeanVarNormalizer{col=Features}
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 9 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 129 | 5 | 0.9627
|
||||
negative || 7 | 213 | 0.9682
|
||||
||======================
|
||||
Precision || 0.9485 | 0.9771 |
|
||||
OVERALL 0/1 ACCURACY: 0.966102
|
||||
LOG LOSS/instance: 0.133256
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.860756
|
||||
AUC: 0.994267
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 96 | 9 | 0.9143
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9697 | 0.9609 |
|
||||
OVERALL 0/1 ACCURACY: 0.963526
|
||||
LOG LOSS/instance: 0.117262
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.870207
|
||||
AUC: 0.997449
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995858 (0.0016)
|
||||
Accuracy: 0.964814 (0.0013)
|
||||
Positive precision: 0.959113 (0.0106)
|
||||
Positive recall: 0.938486 (0.0242)
|
||||
Negative precision: 0.968967 (0.0081)
|
||||
Negative recall: 0.977394 (0.0092)
|
||||
Log-loss: 0.125259 (0.0080)
|
||||
Log-loss reduction: 0.865481 (0.0047)
|
||||
F1 Score: 0.948366 (0.0072)
|
||||
AUPRC: 0.991982 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 337 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.6098362803459167 Improvement: 0.08331
|
||||
[3] (%Time%) 2 iterations Loss: 0.45359888672828674 Improvement: 0.1417
|
||||
[3] (%Time%) 3 iterations Loss: 0.20857921242713928 Improvement: 0.2204
|
||||
[3] (%Time%) 4 iterations Loss: 0.16340915858745575 Improvement: 0.08846
|
||||
[3] (%Time%) 5 iterations Loss: 0.13761261105537415 Improvement: 0.04142
|
||||
[3] (%Time%) 6 iterations Loss: 0.12767525017261505 Improvement: 0.0178
|
||||
[3] (%Time%) 7 iterations Loss: 0.12204845249652863 Improvement: 0.00867
|
||||
[3] (%Time%) 8 iterations Loss: 0.1062392070889473 Improvement: 0.01402
|
||||
[3] (%Time%) 9 iterations Loss: 0.10295262187719345 Improvement: 0.005971
|
||||
[3] (%Time%) 10 iterations Loss: 0.10169977694749832 Improvement: 0.002432
|
||||
[3] (%Time%) 11 iterations Loss: 0.1010425016283989 Improvement: 0.001101
|
||||
[3] (%Time%) 12 iterations Loss: 0.09985871613025665 Improvement: 0.001163
|
||||
[3] (%Time%) 13 iterations Loss: 0.09840531647205353 Improvement: 0.001381
|
||||
[3] (%Time%) 14 iterations Loss: 0.09822732210159302 Improvement: 0.0004787
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Normalize #2' started.
|
||||
[4] (%Time%) 362 examples
|
||||
[4] 'Normalize #2' finished in %Time%.
|
||||
[5] 'LBFGS data prep #2' started.
|
||||
[5] 'LBFGS data prep #2' finished in %Time%.
|
||||
[6] 'LBFGS Optimizer #2' started.
|
||||
[6] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[6] (%Time%) 1 iterations Loss: 0.6230149269104004 Improvement: 0.07013
|
||||
[6] (%Time%) 2 iterations Loss: 0.5622537136077881 Improvement: 0.06264
|
||||
[6] (%Time%) 3 iterations Loss: 0.3308645784854889 Improvement: 0.1912
|
||||
[6] (%Time%) 4 iterations Loss: 0.18456830084323883 Improvement: 0.1574
|
||||
[6] (%Time%) 5 iterations Loss: 0.15308260917663574 Improvement: 0.06287
|
||||
[6] (%Time%) 6 iterations Loss: 0.13650739192962646 Improvement: 0.02814
|
||||
[6] (%Time%) 7 iterations Loss: 0.1291535645723343 Improvement: 0.01255
|
||||
[6] (%Time%) 8 iterations Loss: 0.12108723819255829 Improvement: 0.009187
|
||||
[6] (%Time%) 9 iterations Loss: 0.11698169261217117 Improvement: 0.005376
|
||||
[6] (%Time%) 10 iterations Loss: 0.11253994703292847 Improvement: 0.004675
|
||||
[6] (%Time%) 11 iterations Loss: 0.1117711216211319 Improvement: 0.001745
|
||||
[6] (%Time%) 12 iterations Loss: 0.111464723944664 Improvement: 0.0006662
|
||||
[6] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995858 0.964814 0.959113 0.938486 0.968967 0.977394 0.125259 0.865481 0.948366 0.991982 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=MeanVarNormalizer{col=Features} /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 10.240182 0.9999643 5.150972346940456E-05 1
|
||||
6 0 -1.1200571 0.24600069 0.40736489593415492 0
|
||||
8 0 -3.9487858 0.01891348 0.027547723824485931 0
|
||||
9 0 -4.3041377 0.013332378 0.019363928162219202 0
|
||||
10 0 -4.9245176 0.0072138133 0.010445052394388471 0
|
||||
11 0 -5.023263 0.006539959 0.0094661543908224309 0
|
||||
18 1 6.1077223 0.9977793 0.0032073413870116912 1
|
||||
20 1 6.1394157 0.99784845 0.0031073729064209047 1
|
||||
21 1 5.79043 0.99695265 0.0044031048108654561 1
|
||||
25 1 0.83644867 0.6977167 0.51928670283371614 1
|
||||
28 0 -5.023263 0.006539959 0.0094661543908224309 0
|
||||
31 0 -4.5385065 0.010576305 0.015339644876315672 0
|
||||
32 1 5.9171133 0.9973143 0.003879895635620469 1
|
||||
35 0 -5.023263 0.006539959 0.0094661543908224309 0
|
||||
37 0 -1.3811293 0.2008277 0.32342152114162104 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.9997759 0.88077354 0.18315695939082394 1
|
||||
44 1 6.1460505 0.99786264 0.0030868629883528767 1
|
||||
45 0 -4.9285326 0.007185116 0.010403350700356633 0
|
||||
46 1 3.6651945 0.9750398 0.036467015061163045 1
|
||||
48 0 -3.465179 0.0303194 0.044418474255629364 0
|
||||
50 1 1.7321749 0.8496904 0.23499086765459262 1
|
||||
51 1 -0.5320287 0.37004384 1.4342318788707105 0
|
||||
52 1 3.3840666 0.96720284 0.048109610770621473 1
|
||||
54 1 5.94145 0.99737865 0.0037867779125958421 1
|
||||
56 1 3.8859549 0.9798847 0.029316116563925798 1
|
||||
60 1 1.1627445 0.76183105 0.39245701495087315 1
|
||||
63 1 -0.60947704 0.3521785 1.50562120015828 0
|
||||
64 0 -5.3183413 0.004876975 0.0070532007598482728 0
|
||||
66 0 -3.94835 0.018921569 0.02755961949634533 0
|
||||
68 1 9.436265 0.99992025 0.00011506098160322404 1
|
||||
69 0 -4.7037826 0.008979576 0.013013304155307737 0
|
||||
70 0 -3.626728 0.025913702 0.037878503571533474 0
|
||||
71 1 7.2452755 0.99928695 0.001029081163103395 1
|
||||
72 0 -1.7539306 0.14755212 0.23031646280745757 0
|
||||
73 1 6.5814486 0.9986161 0.001997929380830338 1
|
||||
74 1 1.4721107 0.813378 0.29800216696333026 1
|
||||
76 0 -4.5513906 0.010442327 0.015144302269897698 0
|
||||
77 0 -3.638505 0.02561808 0.037440731183465928 0
|
||||
79 0 -4.93233 0.007158077 0.010364059762995995 0
|
||||
82 0 -3.5243025 0.028628604 0.041905091703152243 0
|
||||
88 0 -3.94835 0.018921569 0.02755961949634533 0
|
||||
90 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
91 0 -4.971357 0.0068859877 0.0099687421604731982 0
|
||||
92 0 -3.94835 0.018921569 0.02755961949634533 0
|
||||
93 0 -5.3183413 0.004876975 0.0070532007598482728 0
|
||||
95 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
96 0 -5.2664347 0.005135473 0.0074280107742848052 0
|
||||
97 0 -3.6532717 0.025252048 0.036898876335042144 0
|
||||
98 1 7.311146 0.99933237 0.00096351050700135713 1
|
||||
99 1 9.038476 0.99988127 0.00017130489018547468 1
|
||||
100 1 3.0386057 0.95428807 0.067503264882898095 1
|
||||
102 0 -3.6926994 0.024299512 0.035489744577618054 0
|
||||
104 1 10.246488 0.99996454 5.1165745927591405E-05 1
|
||||
105 1 2.0713453 0.88808674 0.17122750865212491 1
|
||||
106 1 7.1730757 0.9992336 0.0011061003722974585 1
|
||||
108 0 -5.0587378 0.0063134613 0.0091372737320939128 0
|
||||
109 1 5.668496 0.99655885 0.0049730987086613783 1
|
||||
111 1 2.7006435 0.9370646 0.093779604444945541 1
|
||||
112 1 7.457404 0.9994232 0.00083237813470174872 1
|
||||
113 1 7.8064146 0.9995931 0.00058718225519298383 1
|
||||
115 0 -4.1526413 0.015479452 0.022506777819007655 0
|
||||
117 1 8.846686 0.9998562 0.00020751199143437284 1
|
||||
120 0 -4.3363485 0.012915232 0.018754110705898226 0
|
||||
121 0 -3.406767 0.03208465 0.047047216125628954 0
|
||||
122 1 7.975007 0.99965614 0.00049616926748612491 1
|
||||
123 1 3.1155128 0.9575281 0.062613247752723478 1
|
||||
125 0 -5.3183413 0.004876975 0.0070532007598482728 0
|
||||
128 1 3.2928896 0.96418405 0.05261953701353065 1
|
||||
129 0 -5.075633 0.006208346 0.0089846692284033178 0
|
||||
131 0 -4.5385065 0.010576305 0.015339644876315672 0
|
||||
132 1 7.1590242 0.9992228 0.0011216768239618953 1
|
||||
133 0 -4.5983834 0.0099677425 0.014452562629723156 0
|
||||
137 0 -5.212942 0.005416134 0.0078350666181590742 0
|
||||
138 0 -4.177457 0.015105779 0.021959309852859529 0
|
||||
141 0 -5.5080204 0.0040377555 0.0058370421170140137 0
|
||||
144 0 -5.023263 0.006539959 0.0094661543908224309 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -4.814794 0.008043666 0.011651480971102541 0
|
||||
150 0 -4.9245176 0.0072138133 0.010445052394388471 0
|
||||
151 1 4.048602 0.9828524 0.024953319944645107 1
|
||||
152 1 7.404646 0.999392 0.00087746429875136403 1
|
||||
154 0 -5.8030987 0.0030091058 0.004347766745425947 0
|
||||
156 0 -4.9337225 0.007148188 0.010349690479271116 0
|
||||
161 0 -3.8185477 0.021487804 0.031338262622995577 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 7.072035 0.99915224 0.0012235733412714462 1
|
||||
169 0 -5.6524463 0.003496653 0.0050534440765162749 0
|
||||
171 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
173 1 13.437144 0.99999857 2.0637932864940443E-06 1
|
||||
174 1 4.8828974 0.9924819 0.01088732137979174 1
|
||||
176 0 -4.5385065 0.010576305 0.015339644876315672 0
|
||||
177 1 3.7544203 0.97712165 0.033389906677476572 1
|
||||
179 1 1.6763983 0.842427 0.24737639282930343 1
|
||||
180 0 -4.9245176 0.0072138133 0.010445052394388471 0
|
||||
181 0 -5.8030987 0.0030091058 0.004347766745425947 0
|
||||
183 1 7.56413 0.99948156 0.00074814650034881868 1
|
||||
187 1 12.093892 0.9999944 8.083207235017858E-06 1
|
||||
188 1 7.42859 0.99940634 0.00085672792998639383 1
|
||||
189 0 -4.1933146 0.01487166 0.021616406999206299 0
|
||||
191 1 8.801465 0.9998495 0.00021714443743073658 1
|
||||
192 0 -4.1380286 0.01570373 0.022835467823954694 0
|
||||
196 0 5.2471647 0.9947651 7.5776230955189483 1
|
||||
198 0 -5.8030987 0.0030091058 0.004347766745425947 0
|
||||
199 0 -4.728185 0.008765 0.01270096556832097 0
|
||||
201 1 8.10399 0.99969774 0.00043612792554004811 1
|
||||
202 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
204 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
205 1 10.484727 0.99997205 4.0330495340952001E-05 1
|
||||
206 1 4.135545 0.9842578 0.022891829144370331 1
|
||||
207 0 -4.9245176 0.0072138133 0.010445052394388471 0
|
||||
209 0 -3.7442045 0.023107836 0.033728778427523386 0
|
||||
210 1 12.395179 0.9999959 5.9334136560112192E-06 1
|
||||
211 1 7.6613398 0.99952954 0.00067888923908750794 1
|
||||
212 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
216 0 -5.3183413 0.004876975 0.0070532007598482728 0
|
||||
218 1 6.3693285 0.99828964 0.0024696336612960181 1
|
||||
219 0 -2.8468933 0.05484213 0.081372770652280504 0
|
||||
223 1 4.5218496 0.989248 0.015595883910180993 1
|
||||
226 1 7.9542475 0.9996489 0.0005065778344789803 1
|
||||
228 0 -4.9245176 0.0072138133 0.010445052394388471 0
|
||||
233 1 4.669053 0.990706 0.013471066688781789 1
|
||||
237 1 4.784465 0.99171066 0.012008827269616457 1
|
||||
239 1 4.3852997 0.98769414 0.017863738188117241 1
|
||||
240 0 -2.4713402 0.07789192 0.11699224194288103 0
|
||||
241 0 -3.8511906 0.020812068 0.030342317438039595 0
|
||||
242 0 -4.5385065 0.010576305 0.015339644876315672 0
|
||||
244 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
246 1 9.218602 0.9999008 0.00014309666195095306 1
|
||||
247 1 1.9463377 0.87504673 0.19256803177214576 1
|
||||
248 0 -3.2993407 0.03559381 0.052287186168720752 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -5.418479 0.004414313 0.0063826052804076437 0
|
||||
252 0 3.4806728 0.9701328 5.0652955368832773 1
|
||||
254 1 4.863984 0.9923395 0.011094325676462969 1
|
||||
257 0 -4.728185 0.008765 0.01270096556832097 0
|
||||
258 0 -4.243428 0.014155041 0.020567319566846282 0
|
||||
259 0 2.2049484 0.900693 3.3319607657938701 1
|
||||
260 1 7.8554087 0.9996125 0.00055913794375084334 1
|
||||
262 1 7.738673 0.9995645 0.00062838945639490486 1
|
||||
267 1 3.0221853 0.9535664 0.068594734962942897 1
|
||||
268 1 8.016072 0.99966997 0.00047621255567032231 1
|
||||
269 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
271 0 -3.6532717 0.025252048 0.036898876335042144 0
|
||||
272 1 3.0221853 0.9535664 0.068594734962942897 1
|
||||
275 0 ? ? ? 0
|
||||
276 0 -4.728185 0.008765 0.01270096556832097 0
|
||||
277 0 -5.3183413 0.004876975 0.0070532007598482728 0
|
||||
278 0 -4.833585 0.007895114 0.011435443371134231 0
|
||||
279 1 5.909951 0.997295 0.0039077458999969804 1
|
||||
280 0 -4.243428 0.014155041 0.020567319566846282 0
|
||||
283 1 4.429923 0.9882249 0.017088653030186404 1
|
||||
284 1 5.532611 0.99605995 0.0056955122572893884 1
|
||||
285 1 12.07866 0.99999434 8.1691990448100777E-06 1
|
||||
288 1 1.1231613 0.75457466 0.40626444898693176 1
|
||||
290 0 -5.8030987 0.0030091058 0.004347766745425947 0
|
||||
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||||
620 0 -4.786523 0.008272408 0.011984199103262204 0
|
||||
623 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
625 0 -3.6095085 0.026351927 0.038527693079181344 0
|
||||
626 1 2.5813665 0.9296527 0.10523625470802143 1
|
||||
628 0 -5.478111 0.0041598375 0.0060138940268913308 0
|
||||
630 0 -3.0017242 0.047348037 0.069978851879896622 0
|
||||
631 0 -3.850089 0.020834528 0.030375409122359791 0
|
||||
632 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
635 0 -4.3274727 0.013028876 0.01892021867853276 0
|
||||
636 1 6.5121975 0.998517 0.0021411382395836007 1
|
||||
637 0 -2.76371 0.059317015 0.0882194858402717 0
|
||||
640 0 -4.1332197 0.015778236 0.022944676025255965 0
|
||||
643 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
646 0 -5.5702825 0.0037949479 0.0054853674126795751 0
|
||||
647 0 -5.5378704 0.003919475 0.0056657174145083034 0
|
||||
648 1 6.7130394 0.9987865 0.0017517604823102005 1
|
||||
650 0 -3.8602276 0.020628698 0.030072173002830743 0
|
||||
651 0 -5.165928 0.0056753703 0.00821115140905152 0
|
||||
655 0 -4.786523 0.008272408 0.011984199103262204 0
|
||||
658 1 5.4193115 0.9955893 0.0063773466831117858 1
|
||||
659 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
662 0 -5.268009 0.0051274346 0.007416354222827052 0
|
||||
663 0 -5.268009 0.0051274346 0.007416354222827052 0
|
||||
664 0 -4.378065 0.012394076 0.017992604911173073 0
|
||||
666 0 -3.3309145 0.034525733 0.050690288329398495 0
|
||||
667 0 -4.5631514 0.010321497 0.014968152963076804 0
|
||||
669 1 5.119461 0.9940563 0.0086005539994496276 1
|
||||
671 0 -4.2103515 0.014624112 0.021253925754598346 0
|
||||
672 0 -5.0313683 0.0064875074 0.0093899865593007146 0
|
||||
673 0 -3.5488434 0.027953984 0.040903483554023644 0
|
||||
674 0 -5.7229567 0.00325937 0.0047099570077323736 0
|
||||
675 0 -3.8592558 0.020648342 0.030101109730243672 0
|
||||
676 0 -5.495483 0.004088489 0.0059105334471794332 0
|
||||
677 0 -4.0734596 0.01673363 0.024345794134704377 0
|
||||
684 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
686 0 -5.9463277 0.002608605 0.0037683386354534316 0
|
||||
687 0 -4.391388 0.012232054 0.017755942260436284 0
|
||||
690 0 -5.5378704 0.003919475 0.0056657174145083034 0
|
||||
695 0 -5.478111 0.0041598375 0.0060138940268913308 0
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=MeanVarNormalizer{col=Features}
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 229 | 10 | 0.9582
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9582 | 0.9775 |
|
||||
OVERALL 0/1 ACCURACY: 0.970717
|
||||
LOG LOSS/instance: 0.110699
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.881479
|
||||
AUC: 0.996231
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996231 (0.0000)
|
||||
Accuracy: 0.970717 (0.0000)
|
||||
Positive precision: 0.958159 (0.0000)
|
||||
Positive recall: 0.958159 (0.0000)
|
||||
Negative precision: 0.977477 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.110699 (0.0000)
|
||||
Log-loss reduction: 0.881479 (0.0000)
|
||||
F1 Score: 0.958159 (0.0000)
|
||||
AUPRC: 0.992209 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 699 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.6767677664756775 Improvement: 0.01638
|
||||
[3] (%Time%) 2 iterations Loss: 0.5105687975883484 Improvement: 0.1362
|
||||
[3] (%Time%) 3 iterations Loss: 0.29728010296821594 Improvement: 0.1949
|
||||
[3] (%Time%) 4 iterations Loss: 0.18677178025245667 Improvement: 0.1314
|
||||
[3] (%Time%) 5 iterations Loss: 0.13676075637340546 Improvement: 0.07029
|
||||
[3] (%Time%) 6 iterations Loss: 0.11334767192602158 Improvement: 0.03512
|
||||
[3] (%Time%) 7 iterations Loss: 0.10087242722511292 Improvement: 0.01814
|
||||
[3] (%Time%) 8 iterations Loss: 0.09591954201459885 Improvement: 0.008249
|
||||
[3] (%Time%) 9 iterations Loss: 0.09307149052619934 Improvement: 0.004198
|
||||
[3] (%Time%) 10 iterations Loss: 0.0926487147808075 Improvement: 0.001367
|
||||
[3] (%Time%) 11 iterations Loss: 0.09261518716812134 Improvement: 0.0003668
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Saving model' started.
|
||||
[4] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996231 0.970717 0.958159 0.958159 0.977477 0.977477 0.110699 0.881479 0.958159 0.992209 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=MeanVarNormalizer{col=Features} /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.764402 0.022656268 0.033062046567057066 0
|
||||
1 0 2.1178465 0.8926257 3.219279227792275 1
|
||||
2 0 -4.3002224 0.013383981 0.019439383092732854 0
|
||||
3 0 1.351202 0.79432607 2.2815691356018739 1
|
||||
4 0 -3.6902494 0.024357665 0.03557573456680866 0
|
||||
5 1 9.391339 0.99991655 0.00012039287885617452 1
|
||||
6 0 -2.3100753 0.09029196 0.13652449436068473 0
|
||||
7 0 -4.8593407 0.0076959096 0.01114579433643849 0
|
||||
8 0 -4.510271 0.010875894 0.015776546371534771 0
|
||||
9 0 -4.4885235 0.011112352 0.016121475560316262 0
|
||||
10 0 -5.6358023 0.0035551297 0.0051381067964120014 0
|
||||
11 0 -5.470518 0.004191409 0.0060596328734653596 0
|
||||
12 1 -0.9088621 0.28723273 1.7997079581219078 0
|
||||
13 0 -4.8360434 0.007875879 0.011407473142551429 0
|
||||
14 1 7.2069426 0.9992591 0.0010692683268665336 1
|
||||
15 1 0.4819479 0.61820775 0.69383634829656904 1
|
||||
16 0 -4.573845 0.010212833 0.014809757789196368 0
|
||||
17 0 -4.2127385 0.014589755 0.02120362371500336 0
|
||||
18 1 5.8910837 0.99724364 0.0039820733861929301 1
|
||||
19 0 -3.3160648 0.035024162 0.051435276221612557 0
|
||||
20 1 5.6787024 0.99659365 0.0049227072468166584 1
|
||||
21 1 6.00146 0.997531 0.0035664232425213858 1
|
||||
22 0 -5.0221815 0.006546989 0.0094763634674003196 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.557749 0.0038426279 0.0055544187911462928 0
|
||||
25 1 0.73566914 0.6760481 0.56480219874222859 1
|
||||
26 0 -5.0149136 0.0065944307 0.0095452598863790244 0
|
||||
27 0 -4.125508 0.015898444 0.023120889967349727 0
|
||||
28 0 -5.470518 0.004191409 0.0060596328734653596 0
|
||||
29 0 -5.7798195 0.0030797587 0.0044500086377529235 0
|
||||
30 0 -5.100235 0.0060583865 0.0087669879961435517 0
|
||||
31 0 -5.1094117 0.0060033766 0.0086871438621152833 0
|
||||
32 1 5.6084137 0.99634653 0.0052804902242588958 1
|
||||
33 0 -4.8481674 0.0077817068 0.011270538848868224 0
|
||||
34 0 -4.77211 0.008391492 0.012157445187676147 0
|
||||
35 0 -5.470518 0.004191409 0.0060596328734653596 0
|
||||
36 1 6.987483 0.9990775 0.001331501994190641 1
|
||||
37 0 -1.8643727 0.1341942 0.20788462186500564 0
|
||||
38 1 4.267375 0.98617524 0.020084064818634548 1
|
||||
39 1 0.9488659 0.72088706 0.47215483212277914 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.219411 0.90197915 0.14883401269836216 1
|
||||
42 1 6.171111 0.99791545 0.00301051350343931 1
|
||||
43 1 -0.40685844 0.39966565 1.3231344985055431 0
|
||||
44 1 6.2917633 0.99815196 0.0026686276732342047 1
|
||||
45 0 -5.5885167 0.0037266323 0.0053864368682504261 0
|
||||
46 1 2.8911076 0.9474051 0.077946656939359635 1
|
||||
47 0 -5.9188547 0.0026810698 0.0038731604979789025 0
|
||||
48 0 -3.6902494 0.024357665 0.03557573456680866 0
|
||||
49 1 3.855667 0.979279 0.030208174631500405 1
|
||||
50 1 1.8026791 0.85847473 0.22015242467281587 1
|
||||
51 1 -0.647871 0.34346947 1.5417462239962578 0
|
||||
52 1 4.110798 0.9838698 0.023460698290304831 1
|
||||
53 1 6.369035 0.9982891 0.002470408909382498 1
|
||||
54 1 5.5637884 0.9961804 0.0055210468949917803 1
|
||||
55 1 3.2420082 0.9623849 0.05531411894986267 1
|
||||
56 1 4.0266676 0.9824788 0.025501821525628736 1
|
||||
57 1 0.9429865 0.71970254 0.474527341250158 1
|
||||
58 1 1.1031628 0.7508522 0.41339909133851532 1
|
||||
59 1 0.7148237 0.6714662 0.57461337593478135 1
|
||||
60 1 1.3507318 0.79424924 0.33233629663720055 1
|
||||
61 0 -5.5580025 0.003841657 0.0055530126705563305 0
|
||||
62 1 4.584176 0.98989105 0.014658344476441425 1
|
||||
63 1 -0.254982 0.43659765 1.1956237454749821 0
|
||||
64 0 -5.9188547 0.0026810698 0.0038731604979789025 0
|
||||
65 1 3.1426783 0.95861924 0.060970203576134502 1
|
||||
66 0 -4.2127385 0.014589755 0.02120362371500336 0
|
||||
67 1 2.7787695 0.9415177 0.086939861568672824 1
|
||||
68 1 8.730279 0.9998384 0.00023314132019407291 1
|
||||
69 0 -5.364597 0.004657552 0.0067351241340696888 0
|
||||
70 0 -3.6253662 0.0259481 0.037929449707202641 0
|
||||
71 1 6.2854233 0.9981402 0.0026855994285032801 1
|
||||
72 0 -2.9881434 0.047964398 0.070912569535617045 0
|
||||
73 1 6.9094496 0.9990027 0.00143952479907214 1
|
||||
74 1 1.6685419 0.8413813 0.24916832126127436 1
|
||||
75 0 -4.392567 0.012217814 0.017735144139689803 0
|
||||
76 0 -5.168642 0.0056600748 0.0081889587752785771 0
|
||||
77 0 -3.6936207 0.02427768 0.035457463505541631 0
|
||||
78 0 -3.9393702 0.019088987 0.027805832350465458 0
|
||||
79 0 -5.3924646 0.004530128 0.0065504417301795164 0
|
||||
80 0 -3.4076486 0.03205728 0.047006421666138644 0
|
||||
81 0 -4.3126 0.013221515 0.019201834880105396 0
|
||||
82 0 -3.6790805 0.024624504 0.035970366575764183 0
|
||||
83 0 -2.8557978 0.054382395 0.080671199800402979 0
|
||||
84 1 7.529311 0.9994632 0.00077464581015663852 1
|
||||
85 1 5.033348 0.99352527 0.0093714373235393323 1
|
||||
86 1 1.2443466 0.7763197 0.36527722916870836 1
|
||||
87 1 4.8999996 0.9926084 0.010703390620846196 1
|
||||
88 0 -4.2127385 0.014589755 0.02120362371500336 0
|
||||
89 0 -5.13122 0.0058746324 0.0085002955934485903 0
|
||||
90 0 -5.557749 0.0038426279 0.0055544187911462928 0
|
||||
91 0 -5.122043 0.0059284694 0.0085784270110850786 0
|
||||
92 0 -4.2127385 0.014589755 0.02120362371500336 0
|
||||
93 0 -5.9188547 0.0026810698 0.0038731604979789025 0
|
||||
94 0 -5.1094117 0.0060033766 0.0086871438621152833 0
|
||||
95 0 -5.557749 0.0038426279 0.0055544187911462928 0
|
||||
96 0 -5.5703797 0.00379458 0.0054848346633098333 0
|
||||
97 0 -3.764402 0.022656268 0.033062046567057066 0
|
||||
98 1 7.3032084 0.99932706 0.00097116886824202733 1
|
||||
99 1 8.5376005 0.9998041 0.0002826811791117021 1
|
||||
100 1 3.7847528 0.97779 0.032403446987586869 1
|
||||
101 1 -1.5499125 0.17509891 2.5137579842968623 0
|
||||
102 0 -3.9626675 0.018657608 0.027171511838981446 0
|
||||
103 1 0.64441776 0.6557514 0.60877909658157459 1
|
||||
104 1 9.941403 0.99995184 6.9482664123768386E-05 1
|
||||
105 1 1.811801 0.8595794 0.21829721250085646 1
|
||||
106 1 6.933302 0.9990262 0.0014056107924672516 1
|
||||
107 1 4.567672 0.9897246 0.01490099135092731 1
|
||||
108 0 -5.532552 0.0039402945 0.0056958724214582852 0
|
||||
109 1 4.981205 0.99318105 0.0098713601407351831 1
|
||||
110 0 -3.3261552 0.034684733 0.050927898106668319 0
|
||||
111 1 2.400898 0.91689575 0.12517038895133073 1
|
||||
112 1 7.238345 0.999282 0.0010362235536657822 1
|
||||
113 1 6.97423 0.99906516 0.0013493187444396134 1
|
||||
114 0 -3.5186567 0.028786026 0.0421389153036031 0
|
||||
115 0 -4.8365507 0.0078719165 0.011401710690062102 0
|
||||
116 0 -1.194603 0.23243672 0.38164239333448396 0
|
||||
117 1 7.413211 0.99939716 0.00086997852129328337 1
|
||||
118 0 -5.4021482 0.004486668 0.0064874580057019795 0
|
||||
119 0 -4.2149014 0.014558691 0.021158144632411474 0
|
||||
120 0 -5.0464334 0.0063911243 0.0092500340283818358 0
|
||||
121 0 -3.7665648 0.022608422 0.032991421302454353 0
|
||||
122 1 7.3983526 0.9993881 0.00088305714634083943 1
|
||||
123 1 2.2310362 0.90300214 0.14719868353066001 1
|
||||
124 1 5.5647726 0.9961842 0.005515608679976431 1
|
||||
125 0 -5.9188547 0.0026810698 0.0038731604979789025 0
|
||||
126 1 6.4833326 0.99847364 0.0022037481416414505 1
|
||||
127 0 -4.661075 0.009367706 0.013578442552607576 0
|
||||
128 1 3.5909872 0.9731687 0.039238218025799618 1
|
||||
129 0 -5.6555305 0.003485923 0.0050379097235212136 0
|
||||
130 0 -3.6253662 0.0259481 0.037929449707202641 0
|
||||
131 0 -5.1094117 0.0060033766 0.0086871438621152833 0
|
||||
132 1 6.4476175 0.9984182 0.002283844549890808 1
|
||||
133 0 -4.8290296 0.007930874 0.011487445629105222 0
|
||||
134 0 -4.944127 0.0070747235 0.010242944363114955 0
|
||||
135 0 -3.0172458 0.04665282 0.068926399422304843 0
|
||||
136 0 -4.573845 0.010212833 0.014809757789196368 0
|
||||
137 0 -5.3832874 0.0045717023 0.0066106948866831083 0
|
||||
138 0 -4.323774 0.013076525 0.018989871178379494 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -5.3832874 0.0045717023 0.0066106948866831083 0
|
||||
141 0 -5.831624 0.00292473 0.0042256760653937393 0
|
||||
142 1 3.161852 0.9593732 0.059835992278938759 1
|
||||
143 0 -4.8365507 0.0078719165 0.011401710690062102 0
|
||||
144 0 -5.470518 0.004191409 0.0060596328734653596 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 0.055706024 0.5139229 0.96037607240274647 1
|
||||
147 0 -5.479948 0.0041522332 0.0060028776302925467 0
|
||||
148 0 -1.5959892 0.16854292 0.26628630437021206 0
|
||||
149 1 9.112636 0.99988973 0.00015909272367469463 1
|
||||
150 0 -5.6358023 0.0035551297 0.0051381067964120014 0
|
||||
151 1 3.4900389 0.970403 0.043344061119002616 1
|
||||
152 1 7.445306 0.9994162 0.00084253100292199401 1
|
||||
153 0 -4.213246 0.014582462 0.021192946125694372 0
|
||||
154 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
155 1 2.209117 0.90106523 0.15029654475945994 1
|
||||
156 0 -5.5834627 0.0037454437 0.0054136776374676052 0
|
||||
157 0 -5.1094117 0.0060033766 0.0086871438621152833 0
|
||||
158 0 ? ? ? 0
|
||||
159 1 9.879012 0.99994874 7.3954435339176224E-05 1
|
||||
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||||
664 0 -4.4996924 0.010990285 0.015943402760824361 0
|
||||
665 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
666 0 -3.559639 0.027662132 0.040470386114361039 0
|
||||
667 0 -4.661075 0.009367706 0.013578442552607576 0
|
||||
668 1 1.6534204 0.8393528 0.25265078074486746 1
|
||||
669 1 5.9432 0.99738324 0.0037801391933475611 1
|
||||
670 1 4.219963 0.98551375 0.021052099607391267 1
|
||||
671 0 -4.3145094 0.013196629 0.019165450206738887 0
|
||||
672 0 -5.1094117 0.0060033766 0.0086871438621152833 0
|
||||
673 0 -3.9878473 0.01820212 0.026502044041014937 0
|
||||
674 0 -5.9188547 0.0026810698 0.0038731604979789025 0
|
||||
675 0 -4.184738 0.0149978325 0.021801195626867512 0
|
||||
676 0 -5.668784 0.0034401843 0.004971693491602939 0
|
||||
677 0 -4.486614 0.011133351 0.016152112145348289 0
|
||||
678 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
679 0 -5.831624 0.00292473 0.0042256760653937393 0
|
||||
680 1 13.576985 0.99999875 1.8058189642293044E-06 1
|
||||
681 1 7.3577566 0.99936277 0.00091962629954733767 1
|
||||
682 0 -3.57125 0.027351538 0.040009619134198614 0
|
||||
683 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
684 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
685 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
686 0 -6.279961 0.0018699701 0.0027003221638951715 0
|
||||
687 0 -4.635878 0.009604448 0.013923260317925977 0
|
||||
688 0 -4.934951 0.007139476 0.010337031202055988 0
|
||||
689 0 -3.9675932 0.018567633 0.027039243383364869 0
|
||||
690 0 -5.757472 0.0031491392 0.0045504162627311925 0
|
||||
691 1 2.8391848 0.9447569 0.081984906351014283 1
|
||||
692 0 -5.3832874 0.0045717023 0.0066106948866831083 0
|
||||
693 0 -4.691843 0.00908645 0.013168896096777646 0
|
||||
694 0 -4.9443808 0.0070729414 0.010240355040411054 0
|
||||
695 0 -5.831624 0.00292473 0.0042256760653937393 0
|
||||
696 1 4.762826 0.9915309 0.01227036862059858 1
|
||||
697 1 3.072711 0.95575297 0.065290318478461076 1
|
||||
698 1 3.8867006 0.9798994 0.02929444085231582 1
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 228 | 11 | 0.9540
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9580 | 0.9753 |
|
||||
OVERALL 0/1 ACCURACY: 0.969253
|
||||
LOG LOSS/instance: 0.111002
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.881154
|
||||
AUC: 0.996136
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996136 (0.0000)
|
||||
Accuracy: 0.969253 (0.0000)
|
||||
Positive precision: 0.957983 (0.0000)
|
||||
Positive recall: 0.953975 (0.0000)
|
||||
Negative precision: 0.975281 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.111002 (0.0000)
|
||||
Log-loss reduction: 0.881154 (0.0000)
|
||||
F1 Score: 0.955975 (0.0000)
|
||||
AUPRC: 0.991883 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831
|
||||
[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021
|
||||
[2] (%Time%) 3 iterations Loss: 0.5210472345352173 Improvement: 0.08851
|
||||
[2] (%Time%) 4 iterations Loss: 0.3810925781726837 Improvement: 0.1272
|
||||
[2] (%Time%) 5 iterations Loss: 0.3549964427947998 Improvement: 0.05131
|
||||
[2] (%Time%) 6 iterations Loss: 0.34571003913879395 Improvement: 0.01978
|
||||
[2] (%Time%) 7 iterations Loss: 0.3228526711463928 Improvement: 0.02209
|
||||
[2] (%Time%) 8 iterations Loss: 0.24733668565750122 Improvement: 0.06216
|
||||
[2] (%Time%) 9 iterations Loss: 0.22847387194633484 Improvement: 0.02969
|
||||
[2] (%Time%) 10 iterations Loss: 0.16646333038806915 Improvement: 0.05393
|
||||
[2] (%Time%) 11 iterations Loss: 0.12585707008838654 Improvement: 0.04394
|
||||
[2] (%Time%) 12 iterations Loss: 0.10631134361028671 Improvement: 0.02564
|
||||
[2] (%Time%) 13 iterations Loss: 0.09210390597581863 Improvement: 0.01707
|
||||
[2] (%Time%) 14 iterations Loss: 0.08548402786254883 Improvement: 0.009232
|
||||
[2] (%Time%) 15 iterations Loss: 0.08181773126125336 Improvement: 0.005058
|
||||
[2] (%Time%) 16 iterations Loss: 0.0811641737818718 Improvement: 0.001755
|
||||
[2] (%Time%) 17 iterations Loss: 0.0807165652513504 Improvement: 0.0007743
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'Saving model' started.
|
||||
[3] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996136 0.969253 0.957983 0.953975 0.975281 0.977477 0.111002 0.881154 0.955975 0.991883 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
Linear Binary Classification Predictor non-zero weights
|
||||
|
||||
(Bias) -9.578454
|
||||
f0 0.57847303
|
||||
f5 0.40122226
|
||||
f6 0.38146052
|
||||
f3 0.36177918
|
||||
f8 0.23839118
|
||||
f7 0.2354545
|
||||
f4 0.11693436
|
||||
f1 0.115287125
|
||||
f2 0.10376115
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.851943 0.02079674 0.030319734404703255 0
|
||||
1 0 2.683446 0.9360427 3.9667477413886059 1
|
||||
2 0 -4.607667 0.009876545 0.014319673324111819 0
|
||||
3 0 0.9931965 0.7297188 1.8874669526033945 1
|
||||
4 0 -3.7068577 0.023966083 0.034996813287471229 0
|
||||
5 1 10.284526 0.99996585 4.9273870913609948E-05 1
|
||||
6 0 -2.5548348 0.072102346 0.10796240755799669 0
|
||||
7 0 -5.483601 0.0041371556 0.0059810346515218361 0
|
||||
8 0 -5.3967185 0.004510985 0.006522698994153339 0
|
||||
9 0 -4.69659 0.009043808 0.013106814588233793 0
|
||||
10 0 -6.2827697 0.0018647353 0.0026927557457968382 0
|
||||
11 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
12 1 -0.80001926 0.3100214 1.689560288227052 0
|
||||
13 0 -5.363391 0.004663146 0.0067432323413996785 0
|
||||
14 1 8.460618 0.9997884 0.00030530150678416689 1
|
||||
15 1 0.57511425 0.6399424 0.64398602102001679 1
|
||||
16 0 -4.811877 0.008066977 0.011685384746860946 0
|
||||
17 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
18 1 6.6283293 0.9986794 0.0019064829346821315 1
|
||||
19 0 -3.2734704 0.03649261 0.053632360740655312 0
|
||||
20 1 7.0416136 0.9991261 0.0012613560580149764 1
|
||||
21 1 7.160178 0.9992237 0.0011203859514107531 1
|
||||
22 0 -5.39035 0.004539675 0.0065642778675289084 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
25 1 1.14081 0.75782835 0.4000569742868838 1
|
||||
26 0 -5.3919973 0.004532236 0.0065534968447901092 0
|
||||
27 0 -4.2334037 0.014295614 0.020773049972623758 0
|
||||
28 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
29 0 -6.721234 0.0012036001 0.0017374736946093711 0
|
||||
30 0 -5.5072846 0.0040407153 0.0058413294412794697 0
|
||||
31 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
32 1 6.093754 0.99774814 0.0032524156487446949 1
|
||||
33 0 -5.225583 0.005348464 0.0077369112901479421 0
|
||||
34 0 -5.2865887 0.0050335242 0.0072801783481284011 0
|
||||
35 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
36 1 7.802987 0.99959165 0.00058924688862259259 1
|
||||
37 0 -1.7492757 0.14813858 0.23130934617944934 0
|
||||
38 1 5.2506304 0.99478316 0.0075460050647462888 1
|
||||
39 1 1.1475983 0.75907195 0.39769146164957664 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.8377752 0.9446833 0.082097319842374242 1
|
||||
42 1 5.8458834 0.99711657 0.0041659253355904877 1
|
||||
43 1 -0.11609173 0.4710096 1.086171593088358 0
|
||||
44 1 6.8251944 0.99891514 0.0015659777043749736 1
|
||||
45 0 -6.3089046 0.0018167192 0.002623355435988016 0
|
||||
46 1 2.864027 0.94603926 0.080028040059344982 1
|
||||
47 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
48 0 -3.7068577 0.023966083 0.034996813287471229 0
|
||||
49 1 4.0253563 0.9824562 0.025534993831340422 1
|
||||
50 1 1.0239735 0.73574585 0.44272060071120417 1
|
||||
51 1 -0.41847897 0.39688078 1.3332224128426211 0
|
||||
52 1 4.6123466 0.9901691 0.014253155236391302 1
|
||||
53 1 6.7278337 0.99880433 0.0017260180672053755 1
|
||||
54 1 6.7615156 0.99884385 0.0016689386982227548 1
|
||||
55 1 3.8752527 0.9796727 0.029628300481440009 1
|
||||
56 1 3.8615923 0.97939885 0.030031597806911069 1
|
||||
57 1 0.84530735 0.6995818 0.51543533373329709 1
|
||||
58 1 1.3125248 0.7879353 0.34385089452620432 1
|
||||
59 1 0.8640642 0.7035091 0.50735902554640788 1
|
||||
60 1 1.6607227 0.840335 0.25096350146002316 1
|
||||
61 0 -6.146074 0.0021372968 0.0030867673873954411 0
|
||||
62 1 4.648324 0.9905132 0.01375188562506531 1
|
||||
63 1 -0.09595108 0.47603062 1.0708737252461293 0
|
||||
64 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
65 1 3.006091 0.95284855 0.069681165379489027 1
|
||||
66 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
67 1 3.2676182 0.96330106 0.053941337714885858 1
|
||||
68 1 8.467259 0.9997898 0.00030332328907129791 1
|
||||
69 0 -5.930381 0.0026504262 0.0038288330496920203 0
|
||||
70 0 -4.0258813 0.017534735 0.025521692542287384 0
|
||||
71 1 7.056571 0.999139 0.001242679739507861 1
|
||||
72 0 -3.604663 0.02647654 0.038712348923942028 0
|
||||
73 1 8.587247 0.99981356 0.00026900594391058349 1
|
||||
74 1 1.9609795 0.8766389 0.18994541400089412 1
|
||||
75 0 -5.0439367 0.0064069987 0.0092730834313988885 0
|
||||
76 0 -6.2360125 0.0019538214 0.0028215257165629961 0
|
||||
77 0 -3.6410503 0.02555462 0.037346773253671695 0
|
||||
78 0 -4.2064447 0.014680516 0.021336509114484391 0
|
||||
79 0 -5.8518887 0.0028662262 0.0041410277950115777 0
|
||||
80 0 -3.9594064 0.01871741 0.027259431000833452 0
|
||||
81 0 -4.4500976 0.01154264 0.016749362016831207 0
|
||||
82 0 -3.7366562 0.023278845 0.033981350915040766 0
|
||||
83 0 -3.0818253 0.043863203 0.064711052317795656 0
|
||||
84 1 7.5939436 0.99949676 0.00072620750501173098 1
|
||||
85 1 4.4896603 0.9889001 0.016103272997671614 1
|
||||
86 1 1.0730114 0.74516916 0.4243601222554515 1
|
||||
87 1 5.126397 0.9940971 0.0085412989560641906 1
|
||||
88 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
89 0 -5.4901094 0.004110427 0.0059423139120780765 0
|
||||
90 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
91 0 -5.4100313 0.004451595 0.0064366310044003706 0
|
||||
92 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
93 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
94 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
95 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
96 0 -5.988504 0.002501141 0.0036129038620948267 0
|
||||
97 0 -3.851943 0.02079674 0.030319734404703255 0
|
||||
98 1 6.8195353 0.99890894 0.0015749305425817846 1
|
||||
99 1 9.627425 0.99993414 9.5023543910184011E-05 1
|
||||
100 1 4.583886 0.9898882 0.014662514217723768 1
|
||||
101 1 -1.79492 0.14247058 2.8112640259950759 0
|
||||
102 0 -4.326655 0.013039396 0.018935596585198958 0
|
||||
103 1 1.0023098 0.7315125 0.45104560347649852 1
|
||||
104 1 10.427566 0.9999704 4.2738321772051566E-05 1
|
||||
105 1 1.5173788 0.82015216 0.28603649592587116 1
|
||||
106 1 7.5367727 0.9994672 0.00076888130848180966 1
|
||||
107 1 4.881196 0.9924692 0.010905776396151503 1
|
||||
108 0 -6.076387 0.0022911974 0.0033092917294064135 0
|
||||
109 1 5.561818 0.9961729 0.0055319233865206482 1
|
||||
110 0 -3.7000384 0.024126116 0.035233380452737439 0
|
||||
111 1 3.055334 0.95501226 0.066408838155833469 1
|
||||
112 1 8.819066 0.9998521 0.00021336025454789673 1
|
||||
113 1 7.3867483 0.999381 0.00089329641580267009 1
|
||||
114 0 -3.8721094 0.020390011 0.029720610416632383 0
|
||||
115 0 -5.3238673 0.0048502292 0.0070144262560289661 0
|
||||
116 0 -1.0417509 0.26081228 0.43598731018273634 0
|
||||
117 1 7.683057 0.9995397 0.00066426390723600638 1
|
||||
118 0 -5.892443 0.0027526268 0.0039766766732714344 0
|
||||
119 0 -4.49238 0.011070051 0.016059764137712828 0
|
||||
120 0 -5.700295 0.003333827 0.0048177309121103379 0
|
||||
121 0 -3.913907 0.019571658 0.02851590530839811 0
|
||||
122 1 7.630723 0.99951494 0.00069996718403927136 1
|
||||
123 1 2.3401127 0.91214514 0.13266469494279057 1
|
||||
124 1 6.435666 0.9983992 0.0023113195034606027 1
|
||||
125 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
126 1 7.4104433 0.9993955 0.00087238773278814166 1
|
||||
127 0 -5.008889 0.0066340133 0.0096027457405515309 0
|
||||
128 1 4.228979 0.9856419 0.020864512847765148 1
|
||||
129 0 -5.9932814 0.0024892502 0.0035957060165862273 0
|
||||
130 0 -4.0258813 0.017534735 0.025521692542287384 0
|
||||
131 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
132 1 7.1346445 0.9992036 0.0011493878332558814 1
|
||||
133 0 -5.1548953 0.00573797 0.0083019821396417663 0
|
||||
134 0 -5.2734156 0.0050999294 0.0073764686228992259 0
|
||||
135 0 -2.9798117 0.048346292 0.071491400519711396 0
|
||||
136 0 -4.811877 0.008066977 0.011685384746860946 0
|
||||
137 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
138 0 -4.7081156 0.008941098 0.012957290825585515 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
141 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
142 1 3.5913897 0.97317916 0.039222666362297376 1
|
||||
143 0 -5.3238673 0.0048502292 0.0070144262560289661 0
|
||||
144 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 -0.22845078 0.4431344 1.1741837778979956 0
|
||||
147 0 -6.0291395 0.0024017822 0.0034692070784642232 0
|
||||
148 0 -1.1726217 0.23638143 0.38907590094931721 0
|
||||
149 1 9.549237 0.9999288 0.00010276329372282294 1
|
||||
150 0 -6.2827697 0.0018647353 0.0026927557457968382 0
|
||||
151 1 3.7563772 0.97716534 0.033325400653753334 1
|
||||
152 1 8.478381 0.9997921 0.00029996892610530888 1
|
||||
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||||
658 1 6.97503 0.99906594 0.0013481998116201973 1
|
||||
659 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
660 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
661 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
662 0 -6.3397737 0.0017615929 0.0025436825013048455 0
|
||||
663 0 -6.3397737 0.0017615929 0.0025436825013048455 0
|
||||
664 0 -4.6667914 0.009314808 0.01350140698584403 0
|
||||
665 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
666 0 -3.7956452 0.021974668 0.03205626159943735 0
|
||||
667 0 -5.008889 0.0066340133 0.0096027457405515309 0
|
||||
668 1 1.7405148 0.8507524 0.23318875629408592 1
|
||||
669 1 6.25257 0.9980782 0.0027751998214810445 1
|
||||
670 1 4.575281 0.9898017 0.014788567710774095 1
|
||||
671 0 -4.531823 0.010646472 0.015441959837164124 0
|
||||
672 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
673 0 -4.1635942 0.015313414 0.022263489777284012 0
|
||||
674 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
675 0 -4.882054 0.0075243814 0.010896433785374512 0
|
||||
676 0 -6.443535 0.001588247 0.0022931776451933857 0
|
||||
677 0 -4.614864 0.009806413 0.014217489484295248 0
|
||||
678 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
679 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
680 1 14.449334 0.99999946 7.7392213646475052E-07 1
|
||||
681 1 7.637349 0.99951816 0.00069532140644958649 1
|
||||
682 0 -3.6164885 0.02617343 0.038263230433727313 0
|
||||
683 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
684 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
685 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
686 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
687 0 -4.9194407 0.007250264 0.010498022531001128 0
|
||||
688 0 -5.193337 0.0055227736 0.007989761271866137 0
|
||||
689 0 -5.2600183 0.005168359 0.00747570083968429 0
|
||||
690 0 -6.2051983 0.0020148407 0.0029097330248878416 0
|
||||
691 1 2.4931898 0.923663 0.11456148551173541 1
|
||||
692 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
693 0 -5.1519585 0.0057547493 0.0083263292769673487 0
|
||||
694 0 -5.2536535 0.0052011875 0.0075233094172650833 0
|
||||
695 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
696 1 4.4949503 0.98895806 0.016018753724303419 1
|
||||
697 1 3.1488342 0.9588628 0.060603722373998026 1
|
||||
698 1 3.7653837 0.97736543 0.033030012232047976 1
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 198 | 41 | 0.8285
|
||||
negative || 3 | 441 | 0.9932
|
||||
||======================
|
||||
Precision || 0.9851 | 0.9149 |
|
||||
OVERALL 0/1 ACCURACY: 0.935578
|
||||
LOG LOSS/instance: 0.111002
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.881154
|
||||
AUC: 0.996136
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996136 (0.0000)
|
||||
Accuracy: 0.935578 (0.0000)
|
||||
Positive precision: 0.985075 (0.0000)
|
||||
Positive recall: 0.828452 (0.0000)
|
||||
Negative precision: 0.914938 (0.0000)
|
||||
Negative recall: 0.993243 (0.0000)
|
||||
Log-loss: 0.111002 (0.0000)
|
||||
Log-loss reduction: 0.881154 (0.0000)
|
||||
F1 Score: 0.900000 (0.0000)
|
||||
AUPRC: 0.991883 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831
|
||||
[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021
|
||||
[2] (%Time%) 3 iterations Loss: 0.5210472345352173 Improvement: 0.08851
|
||||
[2] (%Time%) 4 iterations Loss: 0.3810925781726837 Improvement: 0.1272
|
||||
[2] (%Time%) 5 iterations Loss: 0.3549964427947998 Improvement: 0.05131
|
||||
[2] (%Time%) 6 iterations Loss: 0.34571003913879395 Improvement: 0.01978
|
||||
[2] (%Time%) 7 iterations Loss: 0.3228526711463928 Improvement: 0.02209
|
||||
[2] (%Time%) 8 iterations Loss: 0.24733668565750122 Improvement: 0.06216
|
||||
[2] (%Time%) 9 iterations Loss: 0.22847387194633484 Improvement: 0.02969
|
||||
[2] (%Time%) 10 iterations Loss: 0.16646333038806915 Improvement: 0.05393
|
||||
[2] (%Time%) 11 iterations Loss: 0.12585707008838654 Improvement: 0.04394
|
||||
[2] (%Time%) 12 iterations Loss: 0.10631134361028671 Improvement: 0.02564
|
||||
[2] (%Time%) 13 iterations Loss: 0.09210390597581863 Improvement: 0.01707
|
||||
[2] (%Time%) 14 iterations Loss: 0.08548402786254883 Improvement: 0.009232
|
||||
[2] (%Time%) 15 iterations Loss: 0.08181773126125336 Improvement: 0.005058
|
||||
[2] (%Time%) 16 iterations Loss: 0.0811641737818718 Improvement: 0.001755
|
||||
[2] (%Time%) 17 iterations Loss: 0.0807165652513504 Improvement: 0.0007743
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'Saving model' started.
|
||||
[3] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996136 0.935578 0.985075 0.828452 0.914938 0.993243 0.111002 0.881154 0.9 0.991883 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.851943 0.02079674 0.030319734404703255 0
|
||||
1 0 2.683446 0.9360427 3.9667477413886059 0
|
||||
2 0 -4.607667 0.009876545 0.014319673324111819 0
|
||||
3 0 0.9931965 0.7297188 1.8874669526033945 0
|
||||
4 0 -3.7068577 0.023966083 0.034996813287471229 0
|
||||
5 1 10.284526 0.99996585 4.9273870913609948E-05 1
|
||||
6 0 -2.5548348 0.072102346 0.10796240755799669 0
|
||||
7 0 -5.483601 0.0041371556 0.0059810346515218361 0
|
||||
8 0 -5.3967185 0.004510985 0.006522698994153339 0
|
||||
9 0 -4.69659 0.009043808 0.013106814588233793 0
|
||||
10 0 -6.2827697 0.0018647353 0.0026927557457968382 0
|
||||
11 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
12 1 -0.80001926 0.3100214 1.689560288227052 0
|
||||
13 0 -5.363391 0.004663146 0.0067432323413996785 0
|
||||
14 1 8.460618 0.9997884 0.00030530150678416689 1
|
||||
15 1 0.57511425 0.6399424 0.64398602102001679 0
|
||||
16 0 -4.811877 0.008066977 0.011685384746860946 0
|
||||
17 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
18 1 6.6283293 0.9986794 0.0019064829346821315 1
|
||||
19 0 -3.2734704 0.03649261 0.053632360740655312 0
|
||||
20 1 7.0416136 0.9991261 0.0012613560580149764 1
|
||||
21 1 7.160178 0.9992237 0.0011203859514107531 1
|
||||
22 0 -5.39035 0.004539675 0.0065642778675289084 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
25 1 1.14081 0.75782835 0.4000569742868838 0
|
||||
26 0 -5.3919973 0.004532236 0.0065534968447901092 0
|
||||
27 0 -4.2334037 0.014295614 0.020773049972623758 0
|
||||
28 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
29 0 -6.721234 0.0012036001 0.0017374736946093711 0
|
||||
30 0 -5.5072846 0.0040407153 0.0058413294412794697 0
|
||||
31 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
32 1 6.093754 0.99774814 0.0032524156487446949 1
|
||||
33 0 -5.225583 0.005348464 0.0077369112901479421 0
|
||||
34 0 -5.2865887 0.0050335242 0.0072801783481284011 0
|
||||
35 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
36 1 7.802987 0.99959165 0.00058924688862259259 1
|
||||
37 0 -1.7492757 0.14813858 0.23130934617944934 0
|
||||
38 1 5.2506304 0.99478316 0.0075460050647462888 1
|
||||
39 1 1.1475983 0.75907195 0.39769146164957664 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.8377752 0.9446833 0.082097319842374242 0
|
||||
42 1 5.8458834 0.99711657 0.0041659253355904877 1
|
||||
43 1 -0.11609173 0.4710096 1.086171593088358 0
|
||||
44 1 6.8251944 0.99891514 0.0015659777043749736 1
|
||||
45 0 -6.3089046 0.0018167192 0.002623355435988016 0
|
||||
46 1 2.864027 0.94603926 0.080028040059344982 0
|
||||
47 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
48 0 -3.7068577 0.023966083 0.034996813287471229 0
|
||||
49 1 4.0253563 0.9824562 0.025534993831340422 1
|
||||
50 1 1.0239735 0.73574585 0.44272060071120417 0
|
||||
51 1 -0.41847897 0.39688078 1.3332224128426211 0
|
||||
52 1 4.6123466 0.9901691 0.014253155236391302 1
|
||||
53 1 6.7278337 0.99880433 0.0017260180672053755 1
|
||||
54 1 6.7615156 0.99884385 0.0016689386982227548 1
|
||||
55 1 3.8752527 0.9796727 0.029628300481440009 1
|
||||
56 1 3.8615923 0.97939885 0.030031597806911069 1
|
||||
57 1 0.84530735 0.6995818 0.51543533373329709 0
|
||||
58 1 1.3125248 0.7879353 0.34385089452620432 0
|
||||
59 1 0.8640642 0.7035091 0.50735902554640788 0
|
||||
60 1 1.6607227 0.840335 0.25096350146002316 0
|
||||
61 0 -6.146074 0.0021372968 0.0030867673873954411 0
|
||||
62 1 4.648324 0.9905132 0.01375188562506531 1
|
||||
63 1 -0.09595108 0.47603062 1.0708737252461293 0
|
||||
64 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
65 1 3.006091 0.95284855 0.069681165379489027 1
|
||||
66 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
67 1 3.2676182 0.96330106 0.053941337714885858 1
|
||||
68 1 8.467259 0.9997898 0.00030332328907129791 1
|
||||
69 0 -5.930381 0.0026504262 0.0038288330496920203 0
|
||||
70 0 -4.0258813 0.017534735 0.025521692542287384 0
|
||||
71 1 7.056571 0.999139 0.001242679739507861 1
|
||||
72 0 -3.604663 0.02647654 0.038712348923942028 0
|
||||
73 1 8.587247 0.99981356 0.00026900594391058349 1
|
||||
74 1 1.9609795 0.8766389 0.18994541400089412 0
|
||||
75 0 -5.0439367 0.0064069987 0.0092730834313988885 0
|
||||
76 0 -6.2360125 0.0019538214 0.0028215257165629961 0
|
||||
77 0 -3.6410503 0.02555462 0.037346773253671695 0
|
||||
78 0 -4.2064447 0.014680516 0.021336509114484391 0
|
||||
79 0 -5.8518887 0.0028662262 0.0041410277950115777 0
|
||||
80 0 -3.9594064 0.01871741 0.027259431000833452 0
|
||||
81 0 -4.4500976 0.01154264 0.016749362016831207 0
|
||||
82 0 -3.7366562 0.023278845 0.033981350915040766 0
|
||||
83 0 -3.0818253 0.043863203 0.064711052317795656 0
|
||||
84 1 7.5939436 0.99949676 0.00072620750501173098 1
|
||||
85 1 4.4896603 0.9889001 0.016103272997671614 1
|
||||
86 1 1.0730114 0.74516916 0.4243601222554515 0
|
||||
87 1 5.126397 0.9940971 0.0085412989560641906 1
|
||||
88 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
89 0 -5.4901094 0.004110427 0.0059423139120780765 0
|
||||
90 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
91 0 -5.4100313 0.004451595 0.0064366310044003706 0
|
||||
92 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
93 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
94 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
95 0 -6.1658354 0.0020955629 0.0030264303088241035 0
|
||||
96 0 -5.988504 0.002501141 0.0036129038620948267 0
|
||||
97 0 -3.851943 0.02079674 0.030319734404703255 0
|
||||
98 1 6.8195353 0.99890894 0.0015749305425817846 1
|
||||
99 1 9.627425 0.99993414 9.5023543910184011E-05 1
|
||||
100 1 4.583886 0.9898882 0.014662514217723768 1
|
||||
101 1 -1.79492 0.14247058 2.8112640259950759 0
|
||||
102 0 -4.326655 0.013039396 0.018935596585198958 0
|
||||
103 1 1.0023098 0.7315125 0.45104560347649852 0
|
||||
104 1 10.427566 0.9999704 4.2738321772051566E-05 1
|
||||
105 1 1.5173788 0.82015216 0.28603649592587116 0
|
||||
106 1 7.5367727 0.9994672 0.00076888130848180966 1
|
||||
107 1 4.881196 0.9924692 0.010905776396151503 1
|
||||
108 0 -6.076387 0.0022911974 0.0033092917294064135 0
|
||||
109 1 5.561818 0.9961729 0.0055319233865206482 1
|
||||
110 0 -3.7000384 0.024126116 0.035233380452737439 0
|
||||
111 1 3.055334 0.95501226 0.066408838155833469 1
|
||||
112 1 8.819066 0.9998521 0.00021336025454789673 1
|
||||
113 1 7.3867483 0.999381 0.00089329641580267009 1
|
||||
114 0 -3.8721094 0.020390011 0.029720610416632383 0
|
||||
115 0 -5.3238673 0.0048502292 0.0070144262560289661 0
|
||||
116 0 -1.0417509 0.26081228 0.43598731018273634 0
|
||||
117 1 7.683057 0.9995397 0.00066426390723600638 1
|
||||
118 0 -5.892443 0.0027526268 0.0039766766732714344 0
|
||||
119 0 -4.49238 0.011070051 0.016059764137712828 0
|
||||
120 0 -5.700295 0.003333827 0.0048177309121103379 0
|
||||
121 0 -3.913907 0.019571658 0.02851590530839811 0
|
||||
122 1 7.630723 0.99951494 0.00069996718403927136 1
|
||||
123 1 2.3401127 0.91214514 0.13266469494279057 0
|
||||
124 1 6.435666 0.9983992 0.0023113195034606027 1
|
||||
125 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
126 1 7.4104433 0.9993955 0.00087238773278814166 1
|
||||
127 0 -5.008889 0.0066340133 0.0096027457405515309 0
|
||||
128 1 4.228979 0.9856419 0.020864512847765148 1
|
||||
129 0 -5.9932814 0.0024892502 0.0035957060165862273 0
|
||||
130 0 -4.0258813 0.017534735 0.025521692542287384 0
|
||||
131 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
132 1 7.1346445 0.9992036 0.0011493878332558814 1
|
||||
133 0 -5.1548953 0.00573797 0.0083019821396417663 0
|
||||
134 0 -5.2734156 0.0050999294 0.0073764686228992259 0
|
||||
135 0 -2.9798117 0.048346292 0.071491400519711396 0
|
||||
136 0 -4.811877 0.008066977 0.011685384746860946 0
|
||||
137 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
138 0 -4.7081156 0.008941098 0.012957290825585515 0
|
||||
139 0 ? ? ? 0
|
||||
140 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
141 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
142 1 3.5913897 0.97317916 0.039222666362297376 1
|
||||
143 0 -5.3238673 0.0048502292 0.0070144262560289661 0
|
||||
144 0 -5.968823 0.0025507268 0.0036846220745495569 0
|
||||
145 0 ? ? ? 0
|
||||
146 1 -0.22845078 0.4431344 1.1741837778979956 0
|
||||
147 0 -6.0291395 0.0024017822 0.0034692070784642232 0
|
||||
148 0 -1.1726217 0.23638143 0.38907590094931721 0
|
||||
149 1 9.549237 0.9999288 0.00010276329372282294 1
|
||||
150 0 -6.2827697 0.0018647353 0.0026927557457968382 0
|
||||
151 1 3.7563772 0.97716534 0.033325400653753334 1
|
||||
152 1 8.478381 0.9997921 0.00029996892610530888 1
|
||||
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658 1 6.97503 0.99906594 0.0013481998116201973 1
|
||||
659 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
660 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
661 0 -4.430416 0.0117693655 0.017080315839062433 0
|
||||
662 0 -6.3397737 0.0017615929 0.0025436825013048455 0
|
||||
663 0 -6.3397737 0.0017615929 0.0025436825013048455 0
|
||||
664 0 -4.6667914 0.009314808 0.01350140698584403 0
|
||||
665 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
666 0 -3.7956452 0.021974668 0.03205626159943735 0
|
||||
667 0 -5.008889 0.0066340133 0.0096027457405515309 0
|
||||
668 1 1.7405148 0.8507524 0.23318875629408592 0
|
||||
669 1 6.25257 0.9980782 0.0027751998214810445 1
|
||||
670 1 4.575281 0.9898017 0.014788567710774095 1
|
||||
671 0 -4.531823 0.010646472 0.015441959837164124 0
|
||||
672 0 -5.5873623 0.0037309208 0.00539264703325237 0
|
||||
673 0 -4.1635942 0.015313414 0.022263489777284012 0
|
||||
674 0 -6.547296 0.0014319344 0.0020673251758302653 0
|
||||
675 0 -4.882054 0.0075243814 0.010896433785374512 0
|
||||
676 0 -6.443535 0.001588247 0.0022931776451933857 0
|
||||
677 0 -4.614864 0.009806413 0.014217489484295248 0
|
||||
678 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
679 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
680 1 14.449334 0.99999946 7.7392213646475052E-07 1
|
||||
681 1 7.637349 0.99951816 0.00069532140644958649 1
|
||||
682 0 -3.6164885 0.02617343 0.038263230433727313 0
|
||||
683 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
684 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
685 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
686 0 -6.928756 0.0009782601 0.0014120217240218801 0
|
||||
687 0 -4.9194407 0.007250264 0.010498022531001128 0
|
||||
688 0 -5.193337 0.0055227736 0.007989761271866137 0
|
||||
689 0 -5.2600183 0.005168359 0.00747570083968429 0
|
||||
690 0 -6.2051983 0.0020148407 0.0029097330248878416 0
|
||||
691 1 2.4931898 0.923663 0.11456148551173541 0
|
||||
692 0 -5.7718105 0.0031044467 0.0044857363153125709 0
|
||||
693 0 -5.1519585 0.0057547493 0.0083263292769673487 0
|
||||
694 0 -5.2536535 0.0052011875 0.0075233094172650833 0
|
||||
695 0 -6.350283 0.0017432085 0.0025171128231916808 0
|
||||
696 1 4.4949503 0.98895806 0.016018753724303419 1
|
||||
697 1 3.1488342 0.9588628 0.060603722373998026 1
|
||||
698 1 3.7653837 0.97736543 0.033030012232047976 1
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=BinNormalizer{col=Features numBins=5}
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 9 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 129 | 5 | 0.9627
|
||||
negative || 7 | 213 | 0.9682
|
||||
||======================
|
||||
Precision || 0.9485 | 0.9771 |
|
||||
OVERALL 0/1 ACCURACY: 0.966102
|
||||
LOG LOSS/instance: 0.145463
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.848001
|
||||
AUC: 0.992232
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 99 | 6 | 0.9429
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9706 | 0.9736 |
|
||||
OVERALL 0/1 ACCURACY: 0.972644
|
||||
LOG LOSS/instance: 0.123323
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.863498
|
||||
AUC: 0.996769
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.994500 (0.0023)
|
||||
Accuracy: 0.969373 (0.0033)
|
||||
Positive precision: 0.959559 (0.0110)
|
||||
Positive recall: 0.952772 (0.0099)
|
||||
Negative precision: 0.975316 (0.0017)
|
||||
Negative recall: 0.977394 (0.0092)
|
||||
Log-loss: 0.134393 (0.0111)
|
||||
Log-loss reduction: 0.855749 (0.0077)
|
||||
F1 Score: 0.956039 (0.0005)
|
||||
AUPRC: 0.988987 (0.0037)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 337 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.46772801876068115 Improvement: 0.2254
|
||||
[3] (%Time%) 2 iterations Loss: 0.29355108737945557 Improvement: 0.1844
|
||||
[3] (%Time%) 3 iterations Loss: 0.13735580444335938 Improvement: 0.1629
|
||||
[3] (%Time%) 4 iterations Loss: 0.13044576346874237 Improvement: 0.04545
|
||||
[3] (%Time%) 5 iterations Loss: 0.12229779362678528 Improvement: 0.01745
|
||||
[3] (%Time%) 6 iterations Loss: 0.11961209774017334 Improvement: 0.006373
|
||||
[3] (%Time%) 7 iterations Loss: 0.11694043129682541 Improvement: 0.003597
|
||||
[3] (%Time%) 8 iterations Loss: 0.11432048678398132 Improvement: 0.002864
|
||||
[3] (%Time%) 9 iterations Loss: 0.11364643275737762 Improvement: 0.001222
|
||||
[3] (%Time%) 10 iterations Loss: 0.11349356174468994 Improvement: 0.00042
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Normalize #2' started.
|
||||
[4] (%Time%) 362 examples
|
||||
[4] 'Normalize #2' finished in %Time%.
|
||||
[5] 'LBFGS data prep #2' started.
|
||||
[5] 'LBFGS data prep #2' finished in %Time%.
|
||||
[6] 'LBFGS Optimizer #2' started.
|
||||
[6] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[6] (%Time%) 1 iterations Loss: 0.4943659007549286 Improvement: 0.1988
|
||||
[6] (%Time%) 2 iterations Loss: 0.24648860096931458 Improvement: 0.2381
|
||||
[6] (%Time%) 3 iterations Loss: 0.18160340189933777 Improvement: 0.1061
|
||||
[6] (%Time%) 4 iterations Loss: 0.15490023791790009 Improvement: 0.04632
|
||||
[6] (%Time%) 5 iterations Loss: 0.14409959316253662 Improvement: 0.01966
|
||||
[6] (%Time%) 6 iterations Loss: 0.13997505605220795 Improvement: 0.008004
|
||||
[6] (%Time%) 7 iterations Loss: 0.13867685198783875 Improvement: 0.002974
|
||||
[6] (%Time%) 8 iterations Loss: 0.13838209211826324 Improvement: 0.0009646
|
||||
[6] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.9945 0.969373 0.959559 0.952772 0.975316 0.977394 0.134393 0.855749 0.956039 0.988987 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=BinNormalizer{col=Features numBins=5} /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 6.3731 0.998296 0.0024604168548522432 1
|
||||
6 0 -1.5907521 0.1692781 0.26756250697979528 0
|
||||
8 0 -5.2236195 0.00535892 0.0077520772435321107 0
|
||||
9 0 -4.47695 0.011240251 0.016308081037055566 0
|
||||
10 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
11 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
18 1 4.253465 0.9859843 0.020363383207649043 1
|
||||
20 1 3.8221421 0.9785876 0.031227052445240006 1
|
||||
21 1 4.1076407 0.9838196 0.023534291913581692 1
|
||||
25 1 1.3780742 0.7986815 0.3243078025974756 1
|
||||
28 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
31 0 -4.5157695 0.010816903 0.015690507273682066 0
|
||||
32 1 4.4555516 0.98851943 0.016658771299360001 1
|
||||
35 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
37 0 -1.9555035 0.1239545 0.19092228802112898 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.3633351 0.7963012 0.32861388838225319 1
|
||||
44 1 2.931891 0.9494006 0.07491112763797271 1
|
||||
45 0 -5.4227777 0.004395461 0.00635528750479306 0
|
||||
46 1 2.4638524 0.9215686 0.11783657628518621 1
|
||||
48 0 -3.5248084 0.02861454 0.041884202557388027 0
|
||||
50 1 1.0031447 0.7316764 0.45072236954807771 1
|
||||
51 1 0.53688 0.63108635 0.66409067691556545 1
|
||||
52 1 3.071837 0.95571595 0.065346192379893073 1
|
||||
54 1 3.5865636 0.9730529 0.039409827625692173 1
|
||||
56 1 3.0417123 0.95442337 0.067298728664756988 1
|
||||
60 1 2.115437 0.89239454 0.16424640386497277 1
|
||||
63 1 -0.93194675 0.28252992 1.8235244357142577 0
|
||||
64 0 -5.6494246 0.0035071978 0.0050687106135116847 0
|
||||
66 0 -4.0760098 0.016691722 0.024284306549146897 0
|
||||
68 1 5.008427 0.99336296 0.0096071369226945133 1
|
||||
69 0 -4.75636 0.0085235685 0.012349616510985938 0
|
||||
70 0 -3.3575 0.03365042 0.049382912840361233 0
|
||||
71 1 2.8573766 0.94569874 0.080547423238343907 1
|
||||
72 0 -1.8273673 0.1385522 0.21516472317131358 0
|
||||
73 1 4.6069713 0.99011666 0.014329580939977028 1
|
||||
74 1 0.98987293 0.7290628 0.4558850127695051 1
|
||||
76 0 -4.6767793 0.00922309 0.013367847187580073 0
|
||||
77 0 -3.76159 0.022718614 0.03315408153128558 0
|
||||
79 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
82 0 -3.3432946 0.034115426 0.050077302117364809 0
|
||||
88 0 -4.0760098 0.016691722 0.024284306549146897 0
|
||||
90 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
91 0 -5.62796 0.0035830187 0.0051784861200249756 0
|
||||
92 0 -4.0760098 0.016691722 0.024284306549146897 0
|
||||
93 0 -5.6494246 0.0035071978 0.0050687106135116847 0
|
||||
95 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
96 0 -5.62796 0.0035830187 0.0051784861200249756 0
|
||||
97 0 -3.6362498 0.025674434 0.037524173271712737 0
|
||||
98 1 3.2370496 0.96220493 0.055583898838037604 1
|
||||
99 1 5.980808 0.99747956 0.0036408193540462593 1
|
||||
100 1 2.1023402 0.89113045 0.16629145997045666 1
|
||||
102 0 -3.5896869 0.026865304 0.039288585359398748 0
|
||||
104 1 3.9149098 0.9804476 0.028487581411712537 1
|
||||
105 1 1.6840577 0.84344107 0.24564082335087017 1
|
||||
106 1 5.081696 0.99382895 0.0089305236682851747 1
|
||||
108 0 -5.4502554 0.0042768377 0.0061834046775996092 0
|
||||
109 1 3.7112832 0.9761372 0.034844125441217254 1
|
||||
111 1 1.7298045 0.8493874 0.2355053748957035 1
|
||||
112 1 4.5765443 0.9898144 0.014770062959003559 1
|
||||
113 1 4.3831964 0.9876686 0.017901088572706371 1
|
||||
115 0 -3.8197374 0.021462804 0.031301403066249581 0
|
||||
117 1 5.1287656 0.994111 0.0085211441456359803 1
|
||||
120 0 -4.193606 0.014867392 0.02161015763263225 0
|
||||
121 0 -2.9418602 0.050122634 0.074186828046722192 0
|
||||
122 1 4.9073567 0.99266225 0.010625164343991281 1
|
||||
123 1 2.3735156 0.9147853 0.1284948718563525 1
|
||||
125 0 -5.6494246 0.0035071978 0.0050687106135116847 0
|
||||
128 1 1.9842067 0.8791289 0.18585342631939605 1
|
||||
129 0 -6.3433204 0.001755367 0.0025346846160395225 0
|
||||
131 0 -4.5157695 0.010816903 0.015690507273682066 0
|
||||
132 1 4.7996736 0.99183476 0.01182830814689201 1
|
||||
133 0 -5.0104957 0.0066234353 0.009587383133026716 0
|
||||
137 0 -5.9035606 0.002722277 0.0039327711117896919 0
|
||||
138 0 -4.2835827 0.013605494 0.019763330693950525 0
|
||||
141 0 -5.9035606 0.002722277 0.0039327711117896919 0
|
||||
144 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -4.8082304 0.008096207 0.011727897658701648 0
|
||||
150 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
151 1 2.9794164 0.95163554 0.071518943606944177 1
|
||||
152 1 5.4554744 0.9957453 0.0061513281152854489 1
|
||||
154 0 -6.3433204 0.001755367 0.0025346846160395225 0
|
||||
156 0 -4.8701468 0.007613824 0.011026456659264916 0
|
||||
161 0 -3.87684 0.020295732 0.029581769208667251 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 1.6366563 0.83707947 0.25656350809605338 1
|
||||
169 0 -6.06772 0.0023110954 0.0033380645988306942 0
|
||||
171 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
173 1 7.0390973 0.9991239 0.0012645405235460359 1
|
||||
174 1 4.026818 0.9824814 0.025497970436649597 1
|
||||
176 0 -4.5157695 0.010816903 0.015690507273682066 0
|
||||
177 1 1.9759288 0.8782465 0.18730219556696648 1
|
||||
179 1 1.5453286 0.824238 0.27886711286781607 1
|
||||
180 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
181 0 -6.3433204 0.001755367 0.0025346846160395225 0
|
||||
183 1 5.640384 0.9964611 0.0051146183905256002 1
|
||||
187 1 6.207455 0.9979897 0.0029031484906764239 1
|
||||
188 1 4.0257144 0.9824624 0.025525891064657108 1
|
||||
189 0 -3.6899285 0.024365293 0.03558701354174721 0
|
||||
191 1 5.7457566 0.9968139 0.0046039184982092553 1
|
||||
192 0 -4.3301454 0.012994552 0.018870046947394064 0
|
||||
196 0 3.924397 0.9806286 5.6899286855727427 1
|
||||
198 0 -6.3433204 0.001755367 0.0025346846160395225 0
|
||||
199 0 -5.209665 0.005433817 0.0078607169784834265 0
|
||||
201 1 5.2070355 0.99455196 0.007881353643819821 1
|
||||
202 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
204 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
205 1 6.81245 0.9989012 0.001586121668469361 1
|
||||
206 1 3.393744 0.96750844 0.04765385539845407 1
|
||||
207 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
209 0 -3.6362498 0.025674434 0.037524173271712737 0
|
||||
210 1 7.25221 0.9992919 0.0010219388079008645 1
|
||||
211 1 5.5319057 0.99605715 0.0056995698423392338 1
|
||||
212 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
216 0 -5.6494246 0.0035071978 0.0050687106135116847 0
|
||||
218 1 4.1904454 0.98508626 0.021678030585634132 1
|
||||
219 0 -2.6636043 0.065155454 0.097201613664573 0
|
||||
223 1 3.7093024 0.976091 0.034912399504238294 1
|
||||
226 1 5.025266 0.99347305 0.00944725862680992 1
|
||||
228 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
233 1 3.7338624 0.97665757 0.034075274261846249 1
|
||||
237 1 3.4661756 0.9697099 0.044374921922778206 1
|
||||
239 1 3.1221557 0.95779747 0.062207473605672275 1
|
||||
240 0 -1.7659354 0.14604853 0.22777401254807075 0
|
||||
241 0 -3.9645681 0.018622838 0.027120396536089029 0
|
||||
242 0 -4.5157695 0.010816903 0.015690507273682066 0
|
||||
244 0 -4.955529 0.0069950754 0.010127222346273152 0
|
||||
246 1 6.3731 0.998296 0.0024604168548522432 1
|
||||
247 1 1.0848174 0.7474046 0.42003869947012024 1
|
||||
248 0 -3.677671 0.02465838 0.036020473925489939 0
|
||||
249 0 ? ? ? 0
|
||||
250 0 -5.5640426 0.0038186111 0.0055196367606326832 0
|
||||
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|
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|
||||
585 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
587 0 -3.4326403 0.0312908 0.045864450698826768 0
|
||||
588 1 1.747118 0.8515889 0.23177094149351735 1
|
||||
589 0 -3.9901495 0.018161025 0.026441657792382577 0
|
||||
591 1 2.8489904 0.9452665 0.081206990209964802 1
|
||||
592 1 2.371181 0.9146032 0.12878216948776661 1
|
||||
595 0 -3.7405334 0.023190852 0.033851383766504303 0
|
||||
596 0 -3.6373954 0.025645792 0.037481763513402197 0
|
||||
597 0 -2.8868897 0.052805472 0.078267348684659704 0
|
||||
598 0 -4.7423124 0.0086431075 0.01252356788501531 0
|
||||
599 0 -2.2707043 0.09357846 0.1417459459871602 0
|
||||
601 0 -5.2428603 0.005257332 0.0076047350105847766 0
|
||||
603 1 1.345828 0.7934467 0.3337947462851526 1
|
||||
605 1 4.7366858 0.99130857 0.012593892184499829 1
|
||||
608 1 4.951124 0.9929743 0.010171743403134667 1
|
||||
610 1 2.7906847 0.9421704 0.085940115320833135 1
|
||||
611 1 3.2267065 0.96182704 0.056150609805348493 1
|
||||
615 0 -3.6373954 0.025645792 0.037481763513402197 0
|
||||
616 0 -4.7423124 0.0086431075 0.01252356788501531 0
|
||||
620 0 -4.7423124 0.0086431075 0.01252356788501531 0
|
||||
623 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
625 0 -2.8278794 0.055836085 0.082890749604178832 0
|
||||
626 1 3.7322874 0.9766216 0.034128367305907639 1
|
||||
628 0 -4.991929 0.006746721 0.0097664438192888777 0
|
||||
630 0 -2.8750741 0.05339958 0.079172534925648921 0
|
||||
631 0 -3.7405334 0.023190852 0.033851383766504303 0
|
||||
632 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
635 0 -3.7838736 0.02222909 0.032431611512518353 0
|
||||
636 1 3.2124615 0.96130055 0.056940532845114786 1
|
||||
637 0 -1.5607355 0.17354114 0.2749850951563953 0
|
||||
640 0 -4.3096366 0.013260236 0.019258446583421369 0
|
||||
643 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
646 0 -4.377743 0.012398022 0.017998369260801242 0
|
||||
647 0 -5.311416 0.004910699 0.0071020936569294139 0
|
||||
648 1 4.530814 0.98934287 0.015457504426151498 1
|
||||
650 0 -3.3412056 0.034184333 0.050180228383664253 0
|
||||
651 0 -4.5507517 0.010448931 0.015153930412031219 0
|
||||
655 0 -4.7423124 0.0086431075 0.01252356788501531 0
|
||||
658 1 4.899145 0.99260217 0.010712486974836679 1
|
||||
659 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
662 0 -4.6391745 0.009573143 0.013877658797807625 0
|
||||
663 0 -4.6391745 0.009573143 0.013877658797807625 0
|
||||
664 0 -4.56091 0.010344415 0.015001561840162886 0
|
||||
666 0 -2.612207 0.068356924 0.10215074812244168 0
|
||||
667 0 -4.492697 0.011066586 0.016054708591916572 0
|
||||
669 1 4.531974 0.9893551 0.015439686424506834 1
|
||||
671 0 -3.8909473 0.020017117 0.029171544847790533 0
|
||||
672 0 -4.492697 0.011066586 0.016054708591916572 0
|
||||
673 0 -2.875416 0.053382304 0.079146202264596288 0
|
||||
674 0 -5.243202 0.0052555446 0.0076021423142806641 0
|
||||
675 0 -3.282984 0.036159575 0.053133783594803183 0
|
||||
676 0 -4.6391745 0.009573143 0.013877658797807625 0
|
||||
677 0 -3.9901495 0.018161025 0.026441657792382577 0
|
||||
684 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
686 0 -5.4928184 0.0040993528 0.0059262711387168395 0
|
||||
687 0 -4.4344196 0.011722892 0.017012471222298011 0
|
||||
690 0 -5.311416 0.004910699 0.0071020936569294139 0
|
||||
695 0 -4.991929 0.006746721 0.0097664438192888777 0
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=BinNormalizer{col=Features numBins=5}
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 9 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 232 | 7 | 0.9707
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9587 | 0.9841 |
|
||||
OVERALL 0/1 ACCURACY: 0.975110
|
||||
LOG LOSS/instance: 0.116898
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.874842
|
||||
AUC: 0.995208
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995208 (0.0000)
|
||||
Accuracy: 0.975110 (0.0000)
|
||||
Positive precision: 0.958678 (0.0000)
|
||||
Positive recall: 0.970711 (0.0000)
|
||||
Negative precision: 0.984127 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.116898 (0.0000)
|
||||
Log-loss reduction: 0.874842 (0.0000)
|
||||
F1 Score: 0.964657 (0.0000)
|
||||
AUPRC: 0.990065 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 699 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.47700226306915283 Improvement: 0.2161
|
||||
[3] (%Time%) 2 iterations Loss: 0.18854373693466187 Improvement: 0.274
|
||||
[3] (%Time%) 3 iterations Loss: 0.1417279690504074 Improvement: 0.1009
|
||||
[3] (%Time%) 4 iterations Loss: 0.12406111508607864 Improvement: 0.03823
|
||||
[3] (%Time%) 5 iterations Loss: 0.11358059197664261 Improvement: 0.0174
|
||||
[3] (%Time%) 6 iterations Loss: 0.10794322192668915 Improvement: 0.008575
|
||||
[3] (%Time%) 7 iterations Loss: 0.10557964444160461 Improvement: 0.003916
|
||||
[3] (%Time%) 8 iterations Loss: 0.10531868040561676 Improvement: 0.001175
|
||||
[3] (%Time%) 9 iterations Loss: 0.10510236769914627 Improvement: 0.0004559
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Saving model' started.
|
||||
[4] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995208 0.97511 0.958678 0.970711 0.984127 0.977477 0.116898 0.874842 0.964657 0.990065 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=BinNormalizer{col=Features numBins=5} /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.9925442 0.018118374 0.026378988828395827 0
|
||||
1 0 2.2560353 0.90516984 3.3985102900536521 1
|
||||
2 0 -4.274502 0.013727903 0.019942376975914694 0
|
||||
3 0 2.4613528 0.92138773 3.6691017174958245 1
|
||||
4 0 -4.2323236 0.014310841 0.02079533684323219 0
|
||||
5 1 6.468641 0.99845105 0.0022363890442921863 1
|
||||
6 0 -2.3718886 0.0853416 0.12869506224811092 0
|
||||
7 0 -4.447139 0.011576446 0.016798704251788213 0
|
||||
8 0 -4.91697 0.0072680707 0.0105239002904588 0
|
||||
9 0 -4.50454 0.01093772 0.015866725786932372 0
|
||||
10 0 -5.641635 0.0035345273 0.0051082779551064692 0
|
||||
11 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
12 1 0.69736195 0.6676026 0.58293852443354632 1
|
||||
13 0 -4.006762 0.017867163 0.026009927195982187 0
|
||||
14 1 5.0297704 0.9935022 0.0094049332298696709 1
|
||||
15 1 0.4658723 0.61440635 0.70273497463794776 1
|
||||
16 0 -4.9822884 0.0068116337 0.0098607322735206511 0
|
||||
17 0 -4.542241 0.0105372965 0.015282767137702888 0
|
||||
18 1 4.451154 0.9884694 0.016731757772681256 1
|
||||
19 0 -3.9925442 0.018118374 0.026378988828395827 0
|
||||
20 1 3.6021123 0.97345763 0.038809901663734138 1
|
||||
21 1 4.1648107 0.9847049 0.022236640568523543 1
|
||||
22 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.641635 0.0035345273 0.0051082779551064692 0
|
||||
25 1 1.0747519 0.74549955 0.42372061071250477 1
|
||||
26 0 -5.054237 0.0063417605 0.009178360783952514 0
|
||||
27 0 -4.432591 0.011744097 0.017043427201742165 0
|
||||
28 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
29 0 -5.23213 0.0053137476 0.0076865576049069666 0
|
||||
30 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
31 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
32 1 4.808212 0.99190366 0.011728087300114515 1
|
||||
33 0 -4.9369793 0.0071251113 0.010316158421828651 0
|
||||
34 0 -4.8871856 0.0074861557 0.010840868771316861 0
|
||||
35 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
36 1 3.198895 0.96079266 0.057702964236318467 1
|
||||
37 0 -2.6347022 0.06693816 0.099955396837099753 0
|
||||
38 1 3.1199012 0.9577062 0.062344933763709205 1
|
||||
39 1 1.3331995 0.7913694 0.33757685247869501 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.7665772 0.8540315 0.22763880645505633 1
|
||||
42 1 5.7746816 0.99690443 0.0044728861234643764 1
|
||||
43 1 -0.16002464 0.46007898 1.1200465364065422 0
|
||||
44 1 3.312797 0.9648652 0.051600683911559739 1
|
||||
45 0 -5.729995 0.0032365846 0.0046769773550946773 0
|
||||
46 1 2.2845607 0.90759027 0.13988695242272031 1
|
||||
47 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
48 0 -4.2323236 0.014310841 0.02079533684323219 0
|
||||
49 1 3.690333 0.9756443 0.035572842536156273 1
|
||||
50 1 2.3417354 0.9122751 0.13245919284590543 1
|
||||
51 1 0.25731468 0.56397605 0.82629419841938434 1
|
||||
52 1 3.4437442 0.969044 0.045365877126240961 1
|
||||
53 1 4.0185533 0.98233855 0.025707782237024107 1
|
||||
54 1 3.244328 0.96246874 0.055188405707548259 1
|
||||
55 1 3.2247443 0.9617549 0.056258792878492753 1
|
||||
56 1 4.306235 0.9866952 0.019323647565178632 1
|
||||
57 1 0.35732985 0.5883939 0.76514573966057886 1
|
||||
58 1 1.4845958 0.8152657 0.29465774914927734 1
|
||||
59 1 0.8594899 0.70255405 0.50931887952541188 1
|
||||
60 1 2.034841 0.8844069 0.17721777446097878 1
|
||||
61 0 -5.2642455 0.0051466697 0.0074442478551186029 0
|
||||
62 1 3.7685142 0.97743464 0.032927867843541098 1
|
||||
63 1 -0.6011791 0.35407397 1.497877301860955 0
|
||||
64 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
65 1 1.0905423 0.74848384 0.41795693295964292 1
|
||||
66 0 -4.542241 0.0105372965 0.015282767137702888 0
|
||||
67 1 2.2911773 0.9081437 0.13900749596610931 1
|
||||
68 1 5.469751 0.9958054 0.0060642811154188563 1
|
||||
69 0 -5.372775 0.004619792 0.0066803940828329992 0
|
||||
70 0 -3.142992 0.041368302 0.060951450098282248 0
|
||||
71 1 2.9389863 0.94974035 0.07439494629908798 1
|
||||
72 0 -2.570375 0.071069546 0.10635750418723547 0
|
||||
73 1 4.583257 0.9898819 0.014671722439082276 1
|
||||
74 1 1.8871055 0.86842513 0.20352661902691541 1
|
||||
75 0 -3.470492 0.030163588 0.04418667491025341 0
|
||||
76 0 -4.7920833 0.008226915 0.011918021625948197 0
|
||||
77 0 -3.799884 0.021883752 0.031922157151155553 0
|
||||
78 0 -3.4570649 0.030558867 0.044774798675675284 0
|
||||
79 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
80 0 -3.0892968 0.043550916 0.064239925311373264 0
|
||||
81 0 -4.827329 0.0079442635 0.011506917332175943 0
|
||||
82 0 -3.514796 0.028894158 0.042299549715459468 0
|
||||
83 0 -3.3944077 0.032470696 0.047622736922362276 0
|
||||
84 1 3.725741 0.97647166 0.034349917578269849 1
|
||||
85 1 4.4099574 0.9879903 0.017431188449472033 1
|
||||
86 1 1.0602036 0.7427294 0.42909135879947691 1
|
||||
87 1 3.9603305 0.9812996 0.027234453483597865 1
|
||||
88 0 -4.542241 0.0105372965 0.015282767137702888 0
|
||||
89 0 -5.377026 0.004600285 0.0066521210664663259 0
|
||||
90 0 -5.641635 0.0035345273 0.0051082779551064692 0
|
||||
91 0 -5.817073 0.0029674724 0.0042875224490357489 0
|
||||
92 0 -4.542241 0.0105372965 0.015282767137702888 0
|
||||
93 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
94 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
95 0 -5.641635 0.0035345273 0.0051082779551064692 0
|
||||
96 0 -5.817073 0.0029674724 0.0042875224490357489 0
|
||||
97 0 -3.9925442 0.018118374 0.026378988828395827 0
|
||||
98 1 4.950147 0.9929674 0.010181702408864167 1
|
||||
99 1 6.0783668 0.9977133 0.0033027488022180514 1
|
||||
100 1 2.8487382 0.94525343 0.081226912877904262 1
|
||||
101 1 -0.46468973 0.38587388 1.3737986896518364 0
|
||||
102 0 -3.8974419 0.019890115 0.028984588373174795 0
|
||||
103 1 0.83946705 0.69835293 0.51797176632764064 1
|
||||
104 1 4.7606044 0.99151224 0.012297514056213637 1
|
||||
105 1 1.5932136 0.83106774 0.26696201800941971 1
|
||||
106 1 5.222014 0.99463254 0.0077644612468423047 1
|
||||
107 1 3.5070167 0.97088677 0.042625048823229282 1
|
||||
108 0 -5.812822 0.0029800762 0.0043057601542445863 0
|
||||
109 1 3.4048285 0.9678551 0.047137026858474158 1
|
||||
110 0 -3.1308968 0.04185063 0.061677513789817853 0
|
||||
111 1 2.0422235 0.8851595 0.17599066421512091 1
|
||||
112 1 4.0664105 0.98315 0.024516540528572862 1
|
||||
113 1 4.6896644 0.9908939 0.013197505255027312 1
|
||||
114 0 -2.334517 0.08830434 0.13337578836639616 0
|
||||
115 0 -4.069419 0.016800243 0.024443536339947964 0
|
||||
116 0 -0.5892029 0.35681778 0.63670057370933641 0
|
||||
117 1 5.0618005 0.99370575 0.0091093825025296647 1
|
||||
118 0 -4.8868556 0.007488608 0.010844433196401224 0
|
||||
119 0 -3.7967534 0.021950863 0.032021147613601135 0
|
||||
120 0 -4.841877 0.007830427 0.011341380345583197 0
|
||||
121 0 -3.2470562 0.037432812 0.055040849580735463 0
|
||||
122 1 5.884331 0.997225 0.0040090633167083559 1
|
||||
123 1 2.3982568 0.9166943 0.12548741803227695 1
|
||||
124 1 3.7700725 0.97746897 0.032877194241643079 1
|
||||
125 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
126 1 5.009748 0.99337167 0.0095944983615783522 1
|
||||
127 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
128 1 2.5582786 0.9281277 0.10760476775610947 1
|
||||
129 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
130 0 -3.142992 0.041368302 0.060951450098282248 0
|
||||
131 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
132 1 5.628324 0.9964183 0.0051765807671318833 1
|
||||
133 0 -5.2631245 0.0051524127 0.007452576140534177 0
|
||||
134 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
135 0 -2.9062471 0.051845606 0.076806094074945763 0
|
||||
136 0 -4.9822884 0.0068116337 0.0098607322735206511 0
|
||||
137 0 -5.972032 0.0025425751 0.003672831727186712 0
|
||||
138 0 -4.3374887 0.012900705 0.01873287894869256 0
|
||||
139 0 ? ? ? 0
|
||||
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647 0 -6.211811 0.0020015875 0.00289057424438277 0
|
||||
648 1 5.2972035 0.9950194 0.007203474971888885 1
|
||||
649 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
650 0 -4.1821995 0.0150353825 0.021856194718045063 0
|
||||
651 0 -5.294016 0.0049964637 0.0072264417373355955 0
|
||||
652 0 -4.1637306 0.015311358 0.02226047694287395 0
|
||||
653 0 -4.9822884 0.0068116337 0.0098607322735206511 0
|
||||
654 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
655 0 -5.5319853 0.0039425185 0.0056990936690345679 0
|
||||
656 0 -4.432591 0.011744097 0.017043427201742165 0
|
||||
657 0 -0.56492853 0.36240786 0.64929425756523185 0
|
||||
658 1 6.156548 0.9978849 0.0030546337061961703 1
|
||||
659 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
660 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
661 0 -4.542241 0.0105372965 0.015282767137702888 0
|
||||
662 0 -4.7920833 0.008226915 0.011918021625948197 0
|
||||
663 0 -4.7920833 0.008226915 0.011918021625948197 0
|
||||
664 0 -5.2220674 0.0053671994 0.0077640863818058429 0
|
||||
665 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
666 0 -3.243444 0.037563186 0.055236267253767297 0
|
||||
667 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
668 1 1.3892093 0.800466 0.32108796782555399 1
|
||||
669 1 5.3703694 0.99536914 0.0066964411946859766 1
|
||||
670 1 3.0884547 0.956414 0.064292870179836384 1
|
||||
671 0 -4.4592314 0.011438891 0.016597944809862292 0
|
||||
672 0 -5.091938 0.006108553 0.0088398061195112017 0
|
||||
673 0 -3.7500904 0.022975342 0.033533122229986834 0
|
||||
674 0 -6.081682 0.0022791245 0.0032918342260545908 0
|
||||
675 0 -4.132736 0.015785746 0.022955684670604208 0
|
||||
676 0 -5.4368825 0.004334166 0.0062664698465595674 0
|
||||
677 0 -4.8726377 0.0075950255 0.010999127982523411 0
|
||||
678 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
679 0 -5.972032 0.0025425751 0.003672831727186712 0
|
||||
680 1 7.9475226 0.9996466 0.00050993267785952995 1
|
||||
681 1 6.0683713 0.9976904 0.0033359317217204606 1
|
||||
682 0 -3.7236838 0.023575628 0.034419789249732664 0
|
||||
683 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
684 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
685 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
686 0 -6.521729 0.0014689623 0.0021208226987813193 0
|
||||
687 0 -5.2631245 0.0051524127 0.007452576140534177 0
|
||||
688 0 -5.422335 0.0043973983 0.0063580945639077858 0
|
||||
689 0 -5.1149793 0.0059702443 0.0086390562142622321 0
|
||||
690 0 -6.211811 0.0020015875 0.00289057424438277 0
|
||||
691 1 4.3926015 0.9877826 0.017734542916590223 1
|
||||
692 0 -5.972032 0.0025425751 0.003672831727186712 0
|
||||
693 0 -5.180298 0.005594852 0.0080943295732189158 0
|
||||
694 0 -5.1545954 0.0057396814 0.0083044652815304314 0
|
||||
695 0 -5.972032 0.0025425751 0.003672831727186712 0
|
||||
696 1 4.74966 0.9914196 0.012432294804891263 1
|
||||
697 1 2.1880069 0.89916736 0.15333843037334133 1
|
||||
698 1 3.805202 0.97822976 0.031754737784374001 1
|
||||
|
|
@ -0,0 +1,121 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 9 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 129 | 5 | 0.9627
|
||||
negative || 8 | 212 | 0.9636
|
||||
||======================
|
||||
Precision || 0.9416 | 0.9770 |
|
||||
OVERALL 0/1 ACCURACY: 0.963277
|
||||
LOG LOSS/instance: 0.140963
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.852703
|
||||
AUC: 0.994437
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 96 | 9 | 0.9143
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9697 | 0.9609 |
|
||||
OVERALL 0/1 ACCURACY: 0.963526
|
||||
LOG LOSS/instance: 0.111875
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.876170
|
||||
AUC: 0.997066
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995752 (0.0013)
|
||||
Accuracy: 0.963401 (0.0001)
|
||||
Positive precision: 0.955651 (0.0140)
|
||||
Positive recall: 0.938486 (0.0242)
|
||||
Negative precision: 0.968914 (0.0080)
|
||||
Negative recall: 0.975122 (0.0115)
|
||||
Log-loss: 0.126419 (0.0145)
|
||||
Log-loss reduction: 0.864436 (0.0117)
|
||||
F1 Score: 0.946603 (0.0054)
|
||||
AUPRC: 0.991761 (0.0020)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387
|
||||
[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725
|
||||
[2] (%Time%) 3 iterations Loss: 0.6358204483985901 Improvement: 0.009247
|
||||
[2] (%Time%) 4 iterations Loss: 0.6251663565635681 Improvement: 0.01031
|
||||
[2] (%Time%) 5 iterations Loss: 0.5822214484214783 Improvement: 0.03481
|
||||
[2] (%Time%) 6 iterations Loss: 0.24095411598682404 Improvement: 0.2647
|
||||
[2] (%Time%) 7 iterations Loss: 0.18065451085567474 Improvement: 0.1114
|
||||
[2] (%Time%) 8 iterations Loss: 0.12022408843040466 Improvement: 0.07317
|
||||
[2] (%Time%) 9 iterations Loss: 0.09626226872205734 Improvement: 0.03626
|
||||
[2] (%Time%) 10 iterations Loss: 0.08481403440237045 Improvement: 0.01765
|
||||
[2] (%Time%) 11 iterations Loss: 0.07749200612306595 Improvement: 0.009905
|
||||
[2] (%Time%) 12 iterations Loss: 0.07508934289216995 Improvement: 0.004278
|
||||
[2] (%Time%) 13 iterations Loss: 0.07309532910585403 Improvement: 0.002565
|
||||
[2] (%Time%) 14 iterations Loss: 0.07258453965187073 Improvement: 0.001024
|
||||
[2] (%Time%) 15 iterations Loss: 0.07185003906488419 Improvement: 0.000807
|
||||
[2] (%Time%) 16 iterations Loss: 0.07156690955162048 Improvement: 0.0004141
|
||||
[2] (%Time%) 17 iterations Loss: 0.07156465202569962 Improvement: 0.0001052
|
||||
[2] (%Time%) 18 iterations Loss: 0.07146970927715302 Improvement: 9.751E-05
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'LBFGS data prep #2' started.
|
||||
[3] 'LBFGS data prep #2' finished in %Time%.
|
||||
[4] 'LBFGS Optimizer #2' started.
|
||||
[4] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603
|
||||
[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156
|
||||
[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083
|
||||
[4] (%Time%) 4 iterations Loss: 0.6002617478370667 Improvement: 0.01264
|
||||
[4] (%Time%) 5 iterations Loss: 0.5936987996101379 Improvement: 0.008078
|
||||
[4] (%Time%) 6 iterations Loss: 0.5855143666267395 Improvement: 0.008158
|
||||
[4] (%Time%) 7 iterations Loss: 0.5579032301902771 Improvement: 0.02275
|
||||
[4] (%Time%) 8 iterations Loss: 0.514010488986969 Improvement: 0.03861
|
||||
[4] (%Time%) 9 iterations Loss: 0.4704664349555969 Improvement: 0.04231
|
||||
[4] (%Time%) 10 iterations Loss: 0.3519382178783417 Improvement: 0.09947
|
||||
[4] (%Time%) 11 iterations Loss: 0.24160803854465485 Improvement: 0.1076
|
||||
[4] (%Time%) 12 iterations Loss: 0.15622469782829285 Improvement: 0.09094
|
||||
[4] (%Time%) 13 iterations Loss: 0.13706165552139282 Improvement: 0.03711
|
||||
[4] (%Time%) 14 iterations Loss: 0.12257832288742065 Improvement: 0.02014
|
||||
[4] (%Time%) 15 iterations Loss: 0.11211910843849182 Improvement: 0.01288
|
||||
[4] (%Time%) 16 iterations Loss: 0.10575487464666367 Improvement: 0.007993
|
||||
[4] (%Time%) 17 iterations Loss: 0.10021656006574631 Improvement: 0.006152
|
||||
[4] (%Time%) 18 iterations Loss: 0.09685001522302628 Improvement: 0.004063
|
||||
[4] (%Time%) 19 iterations Loss: 0.0928599014878273 Improvement: 0.004008
|
||||
[4] (%Time%) 20 iterations Loss: 0.09131418913602829 Improvement: 0.002161
|
||||
[4] (%Time%) 21 iterations Loss: 0.09053906053304672 Improvement: 0.001122
|
||||
[4] (%Time%) 22 iterations Loss: 0.09002906829118729 Improvement: 0.0006629
|
||||
[4] (%Time%) 23 iterations Loss: 0.08863715082406998 Improvement: 0.00121
|
||||
[4] (%Time%) 24 iterations Loss: 0.0881308913230896 Improvement: 0.0006821
|
||||
[4] (%Time%) 25 iterations Loss: 0.08750966191291809 Improvement: 0.0006364
|
||||
[4] (%Time%) 26 iterations Loss: 0.08728768676519394 Improvement: 0.0003256
|
||||
[4] (%Time%) 27 iterations Loss: 0.08721958100795746 Improvement: 0.0001325
|
||||
[4] (%Time%) 28 iterations Loss: 0.08721008896827698 Improvement: 4.024E-05
|
||||
[4] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995752 0.963401 0.955651 0.938486 0.968914 0.975122 0.126419 0.864436 0.946603 0.991761 0.1 0.0001 1 + LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 12.634016 0.9999967 4.7295306510363196E-06 1
|
||||
6 0 -1.3840265 0.2003631 0.32258304597672599 0
|
||||
8 0 -4.72443 0.008797685 0.01274853746288432 0
|
||||
9 0 -5.1366463 0.005843026 0.0084544286227711037 0
|
||||
10 0 -5.6093 0.0036502592 0.0052758458554607087 0
|
||||
11 0 -5.8696203 0.002815993 0.0040683501047807172 0
|
||||
18 1 7.267023 0.9993023 0.001006879741116309 1
|
||||
20 1 7.564518 0.99948174 0.00074788839258190052 1
|
||||
21 1 7.3895073 0.9993827 0.0008908871693913803 1
|
||||
25 1 1.3487854 0.793931 0.33291445228970418 1
|
||||
28 0 -5.8696203 0.002815993 0.0040683501047807172 0
|
||||
31 0 -5.1809826 0.0055910437 0.0080888046329900768 0
|
||||
32 1 7.291664 0.99931926 0.00098244136155410146 1
|
||||
35 0 -5.8696203 0.002815993 0.0040683501047807172 0
|
||||
37 0 -1.0239582 0.2642571 0.44272638610457016 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.784809 0.9418494 0.086431684776605053 1
|
||||
44 1 7.8207245 0.9995988 0.00057892375102128462 1
|
||||
45 0 -5.7771015 0.003088115 0.0044621015121553741 0
|
||||
46 1 4.363323 0.9874242 0.018258099080425954 1
|
||||
48 0 -3.8101325 0.021665458 0.031600214495233307 0
|
||||
50 1 2.3506079 0.9129825 0.1313408508726798 1
|
||||
51 1 -0.38570404 0.4047519 1.3048902531945354 0
|
||||
52 1 4.7617655 0.991522 0.012283290824972246 1
|
||||
54 1 7.3694706 0.9993702 0.00090887057002802098 1
|
||||
56 1 4.514043 0.9891646 0.015717498447270977 1
|
||||
60 1 2.0933666 0.89025676 0.16770660536078968 1
|
||||
63 1 -0.43266487 0.39349017 1.345600516084799 0
|
||||
64 0 -6.2355585 0.0019547066 0.0028228053227955028 0
|
||||
66 0 -4.4491057 0.011553961 0.016765885815999977 0
|
||||
68 1 12.42049 0.99999595 5.8474219794828371E-06 1
|
||||
69 0 -5.372943 0.0046190205 0.0066792757321367446 0
|
||||
70 0 -3.944561 0.018992033 0.027663241712138437 0
|
||||
71 1 8.002766 0.99966556 0.00048257802859336444 1
|
||||
72 0 -1.4694653 0.18702391 0.29871516855965657 0
|
||||
73 1 8.232639 0.9997342 0.00038348628329455832 1
|
||||
74 1 1.5337801 0.8225587 0.28180945594824713 1
|
||||
76 0 -4.994693 0.006728226 0.0097395799853625745 0
|
||||
77 0 -4.450124 0.01154234 0.016748924320874031 0
|
||||
79 0 -5.807241 0.0029967043 0.0043298212867696499 0
|
||||
82 0 -4.08207 0.016592547 0.024138806297648339 0
|
||||
88 0 -4.4491057 0.011553961 0.016765885815999977 0
|
||||
90 0 -5.546921 0.0038842994 0.0056147713181484132 0
|
||||
91 0 -5.8728333 0.0028069853 0.0040553180261770671 0
|
||||
92 0 -4.4491057 0.011553961 0.016765885815999977 0
|
||||
93 0 -6.2355585 0.0019547066 0.0028228053227955028 0
|
||||
95 0 -5.546921 0.0038842994 0.0056147713181484132 0
|
||||
96 0 -6.238772 0.0019484477 0.0028137579050843478 0
|
||||
97 0 -4.083167 0.016574655 0.024112557476670533 0
|
||||
98 1 8.655586 0.9998259 0.00025120253004900898 1
|
||||
99 1 11.468365 0.99998957 1.5048560434990871E-05 1
|
||||
100 1 3.7766733 0.97761387 0.032663346614426464 1
|
||||
102 0 -4.0354834 0.01737008 0.025279927497574458 0
|
||||
104 1 12.723648 0.999997 4.2995726784359523E-06 1
|
||||
105 1 2.7624168 0.94061077 0.088330249233447317 1
|
||||
106 1 8.566938 0.99980974 0.00027451042551103868 1
|
||||
108 0 -5.8876033 0.0027659447 0.0039959435227908723 0
|
||||
109 1 7.138527 0.9992067 0.0011449127273942801 1
|
||||
111 1 2.7874622 0.94199455 0.086209385178054376 1
|
||||
112 1 9.633659 0.9999345 9.4507562065679747E-05 1
|
||||
113 1 9.298004 0.9999084 0.00013217472169822292 1
|
||||
115 0 -5.0740213 0.006218298 0.0089991161843039711 0
|
||||
117 1 11.032467 0.99998385 2.3303837404214046E-05 1
|
||||
120 0 -4.8138123 0.008051504 0.011662880569639303 0
|
||||
121 0 -3.9854646 0.018244753 0.026564690979039515 0
|
||||
122 1 9.10256 0.99988866 0.00016064073906035849 1
|
||||
123 1 3.8359165 0.9788744 0.030804357887786005 1
|
||||
125 0 -6.2355585 0.0019547066 0.0028228053227955028 0
|
||||
128 1 3.930893 0.98075163 0.028040261476875789 1
|
||||
129 0 -6.4251633 0.0016176477 0.0023356619743196646 0
|
||||
131 0 -5.1809826 0.0055910437 0.0080888046329900768 0
|
||||
132 1 8.021416 0.99967176 0.00047363196654040616 1
|
||||
133 0 -5.3297043 0.004822137 0.0069737005123077563 0
|
||||
137 0 -6.19232 0.002040903 0.0029474094708446014 0
|
||||
138 0 -4.7241216 0.008800375 0.012752453623473303 0
|
||||
141 0 -6.5582585 0.0014163447 0.0020448018331478031 0
|
||||
144 0 -5.8696203 0.002815993 0.0040683501047807172 0
|
||||
145 0 ? ? ? 0
|
||||
147 0 -5.7106357 0.0032996438 0.0047682507744472441 0
|
||||
150 0 -5.6093 0.0036502592 0.0052758458554607087 0
|
||||
151 1 5.1192083 0.9940548 0.0086027166383069592 1
|
||||
152 1 9.233112 0.99990225 0.00014103266690546063 1
|
||||
154 0 -6.9241967 0.0009827262 0.0014184713509792089 0
|
||||
156 0 -5.820839 0.0029563506 0.00427142933878765 0
|
||||
161 0 -4.275128 0.013719428 0.019929979951712175 0
|
||||
164 0 ? ? ? 0
|
||||
167 1 8.727816 0.999838 0.00023374335687970695 1
|
||||
169 0 -6.6670895 0.0012704796 0.0018340800023318104 0
|
||||
171 0 -5.546921 0.0038842994 0.0056147713181484132 0
|
||||
173 1 16.564802 0.99999994 8.5991327994145617E-08 1
|
||||
174 1 5.9242706 0.99733335 0.0038523045725836797 1
|
||||
176 0 -5.1809826 0.0055910437 0.0080888046329900768 0
|
||||
177 1 4.522547 0.98925537 0.01558510513199726 1
|
||||
179 1 1.797656 0.85786337 0.22118020942368269 1
|
||||
180 0 -5.6093 0.0036502592 0.0052758458554607087 0
|
||||
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||||
503 0 -4.5950365 0.010000825 0.014500771836973472 0
|
||||
505 0 -5.8654613 0.002827696 0.0040852817425330737 0
|
||||
506 1 8.127268 0.9997047 0.00042606393366297047 1
|
||||
508 0 -4.6574306 0.009401588 0.013627786320506213 0
|
||||
509 0 -6.566267 0.0014050631 0.0020285029665137564 0
|
||||
511 0 -4.308094 0.0132804345 0.019287978796192751 0
|
||||
512 0 -4.6574306 0.009401588 0.013627786320506213 0
|
||||
515 1 5.3391886 0.99522316 0.0069080292293273596 1
|
||||
516 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
518 0 -5.2989907 0.004971792 0.0071906697545427384 0
|
||||
524 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
525 0 -5.6403575 0.0035390293 0.0051147960404279947 0
|
||||
526 0 -5.2937093 0.004997988 0.0072286522807915041 0
|
||||
530 1 4.635662 0.9903935 0.013926220518781712 1
|
||||
536 0 -3.9587579 0.018729325 0.027276949203992613 0
|
||||
537 0 -3.617391 0.026150433 0.038229162625827671 0
|
||||
542 0 -4.592904 0.010021959 0.014531570820616352 0
|
||||
543 0 -4.944373 0.007072995 0.010240432848577178 0
|
||||
545 0 -4.308094 0.0132804345 0.019287978796192751 0
|
||||
550 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
551 0 -6.503873 0.0014953883 0.00215900401458235 0
|
||||
552 0 -4.0064664 0.017872352 0.026017550034019683 0
|
||||
553 0 -3.045689 0.045403954 0.067037733681634973 0
|
||||
554 0 -5.929988 0.0026514651 0.0038303358354253489 0
|
||||
555 0 -2.6954117 0.06324465 0.094255780132726341 0
|
||||
556 0 -3.644833 0.025460593 0.03720757122717331 0
|
||||
562 0 -6.503873 0.0014953883 0.00215900401458235 0
|
||||
564 0 -4.382785 0.012336438 0.017908408849915267 0
|
||||
567 0 -4.305331 0.013316687 0.019340985242110656 0
|
||||
568 1 2.6287718 0.9326905 0.10052967023149278 1
|
||||
570 1 4.9680624 0.99309146 0.010001498338096082 1
|
||||
571 1 8.770353 0.99984473 0.00022402479537507388 1
|
||||
572 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
573 0 -6.8532095 0.0010549467 0.0015227697088502346 0
|
||||
574 1 5.0985794 0.99393165 0.0087814483182710578 1
|
||||
575 0 -3.617391 0.026150433 0.038229162625827671 0
|
||||
576 0 -4.308094 0.0132804345 0.019287978796192751 0
|
||||
579 0 -6.503873 0.0014953883 0.00215900401458235 0
|
||||
580 0 -3.9667277 0.018583411 0.027062437746958327 0
|
||||
583 0 -5.929988 0.0026514651 0.0038303358354253489 0
|
||||
585 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
587 0 -4.0958424 0.01636931 0.023811345044811066 0
|
||||
588 1 4.5071907 0.9890909 0.015824951986742844 1
|
||||
589 0 -4.6574306 0.009401588 0.013627786320506213 0
|
||||
591 1 3.092105 0.9565659 0.064063707407253548 1
|
||||
592 1 4.69481 0.9909402 0.013130077553168791 1
|
||||
595 0 -4.308094 0.0132804345 0.019287978796192751 0
|
||||
596 0 -4.6030064 0.009922224 0.014386233834222597 0
|
||||
597 0 -3.2760243 0.03640292 0.053498070722816017 0
|
||||
598 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
599 0 -3.601067 0.026569381 0.038849940498209994 0
|
||||
601 0 -6.8532095 0.0010549467 0.0015227697088502346 0
|
||||
603 1 3.5292263 0.971508 0.041702180188336803 1
|
||||
605 1 8.487892 0.99979407 0.0002971306250718485 1
|
||||
608 1 7.3316307 0.9993459 0.00094397756746276323 1
|
||||
610 1 7.064314 0.9991456 0.0012331265087600954 1
|
||||
611 1 4.9257965 0.99279535 0.010431739281620871 1
|
||||
615 0 -4.2616396 0.013903144 0.020198737255684576 0
|
||||
616 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
620 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
623 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
625 0 -4.288692 0.013537096 0.019663295351612649 0
|
||||
626 1 3.0649843 0.955425 0.065785431208875977 1
|
||||
628 0 -6.566267 0.0014050631 0.0020285029665137564 0
|
||||
630 0 -3.3061552 0.03536063 0.051938403513743084 0
|
||||
631 0 -4.308094 0.0132804345 0.019287978796192751 0
|
||||
632 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
635 0 -4.905888 0.007348466 0.010640740363777632 0
|
||||
636 1 8.309126 0.9997538 0.00035527390612860978 1
|
||||
637 0 -2.9892273 0.047914926 0.070837602858330781 0
|
||||
640 0 -4.714448 0.00888516 0.012875863538337475 0
|
||||
643 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
646 0 -6.8289156 0.0010808613 0.0015601965892723959 0
|
||||
647 0 -6.6232843 0.0013272946 0.0019161533025761086 0
|
||||
648 1 8.327719 0.99975836 0.0003486509585214664 1
|
||||
650 0 -4.6817946 0.009177372 0.013301278045886511 0
|
||||
651 0 -6.241685 0.0019427912 0.0028055813863168779 0
|
||||
655 0 -5.5806513 0.003755949 0.0054288906680326756 0
|
||||
658 1 6.517166 0.9985243 0.0021305456363085924 1
|
||||
659 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
662 0 -6.170476 0.0020858808 0.0030124328280602573 0
|
||||
663 0 -6.170476 0.0020858808 0.0030124328280602573 0
|
||||
664 0 -5.0013905 0.0066836136 0.0096747836464466438 0
|
||||
666 0 -3.811726 0.021631705 0.031550441709789802 0
|
||||
667 0 -5.2313156 0.005318054 0.0076928036816938609 0
|
||||
669 1 6.017021 0.997569 0.0035114260343168382 1
|
||||
671 0 -4.9094214 0.007322737 0.010603346693834738 0
|
||||
672 0 -5.867594 0.0028216895 0.004076591553961454 0
|
||||
673 0 -4.2515373 0.014042326 0.020402380515903241 0
|
||||
674 0 -6.8532095 0.0010549467 0.0015227697088502346 0
|
||||
675 0 -4.2696095 0.0137943 0.020039503854497525 0
|
||||
676 0 -6.5118427 0.0014835353 0.0021418781570889814 0
|
||||
677 0 -4.6574306 0.009401588 0.013627786320506213 0
|
||||
684 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
686 0 -7.2025456 0.00074413355 0.0010739574120585704 0
|
||||
687 0 -5.156148 0.005730828 0.0082916185312840122 0
|
||||
690 0 -6.6232843 0.0013272946 0.0019161533025761086 0
|
||||
695 0 -6.566267 0.0014050631 0.0020285029665137564 0
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Not adding a normalizer.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 228 | 11 | 0.9540
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9580 | 0.9753 |
|
||||
OVERALL 0/1 ACCURACY: 0.969253
|
||||
LOG LOSS/instance: 0.109007
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.883291
|
||||
AUC: 0.996287
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996287 (0.0000)
|
||||
Accuracy: 0.969253 (0.0000)
|
||||
Positive precision: 0.957983 (0.0000)
|
||||
Positive recall: 0.953975 (0.0000)
|
||||
Negative precision: 0.975281 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.109007 (0.0000)
|
||||
Log-loss reduction: 0.883291 (0.0000)
|
||||
F1 Score: 0.955975 (0.0000)
|
||||
AUPRC: 0.992293 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'LBFGS data prep' started.
|
||||
[1] 'LBFGS data prep' finished in %Time%.
|
||||
[2] 'LBFGS Optimizer' started.
|
||||
[2] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831
|
||||
[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021
|
||||
[2] (%Time%) 3 iterations Loss: 0.6191107034683228 Improvement: 0.0138
|
||||
[2] (%Time%) 4 iterations Loss: 0.6139997839927673 Improvement: 0.007257
|
||||
[2] (%Time%) 5 iterations Loss: 0.6017453074455261 Improvement: 0.01101
|
||||
[2] (%Time%) 6 iterations Loss: 0.5552627444267273 Improvement: 0.03762
|
||||
[2] (%Time%) 7 iterations Loss: 0.4080789387226105 Improvement: 0.1198
|
||||
[2] (%Time%) 8 iterations Loss: 0.22476643323898315 Improvement: 0.1674
|
||||
[2] (%Time%) 9 iterations Loss: 0.17795847356319427 Improvement: 0.07696
|
||||
[2] (%Time%) 10 iterations Loss: 0.137373685836792 Improvement: 0.04968
|
||||
[2] (%Time%) 11 iterations Loss: 0.1169188842177391 Improvement: 0.02776
|
||||
[2] (%Time%) 12 iterations Loss: 0.1018657311797142 Improvement: 0.01823
|
||||
[2] (%Time%) 13 iterations Loss: 0.0918111726641655 Improvement: 0.0121
|
||||
[2] (%Time%) 14 iterations Loss: 0.08791256695985794 Improvement: 0.005949
|
||||
[2] (%Time%) 15 iterations Loss: 0.08431504666805267 Improvement: 0.004185
|
||||
[2] (%Time%) 16 iterations Loss: 0.0827585905790329 Improvement: 0.002214
|
||||
[2] (%Time%) 17 iterations Loss: 0.0813479945063591 Improvement: 0.001611
|
||||
[2] (%Time%) 18 iterations Loss: 0.08036942780017853 Improvement: 0.001137
|
||||
[2] (%Time%) 19 iterations Loss: 0.07970958948135376 Improvement: 0.0007791
|
||||
[2] (%Time%) 20 iterations Loss: 0.07952725142240524 Improvement: 0.0003315
|
||||
[2] (%Time%) 21 iterations Loss: 0.07951106876134872 Improvement: 9.502E-05
|
||||
[2] 'LBFGS Optimizer' finished in %Time%.
|
||||
[3] 'Saving model' started.
|
||||
[3] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996287 0.969253 0.957983 0.953975 0.975281 0.977477 0.109007 0.883291 0.955975 0.992293 0.1 0.0001 1 + LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.9978414 0.018024378 0.026240884777892562 0
|
||||
1 0 2.2570543 0.9052573 3.3998411691279338 1
|
||||
2 0 -4.68605 0.009138758 0.013245055557552017 0
|
||||
3 0 1.2745533 0.7815212 2.1944348071729731 1
|
||||
4 0 -3.8983564 0.019872293 0.028958355424267362 0
|
||||
5 1 10.383684 0.99996907 4.4630188215668621E-05 1
|
||||
6 0 -2.6780772 0.06427943 0.095850325661809468 0
|
||||
7 0 -5.310401 0.0049156626 0.0071092898061758357 0
|
||||
8 0 -4.9831486 0.0068058167 0.009852282525592778 0
|
||||
9 0 -4.9553494 0.0069963243 0.010129036826825128 0
|
||||
10 0 -6.2179313 0.001989399 0.0028729548666923911 0
|
||||
11 0 -6.0393343 0.0023774789 0.0034340608007065211 0
|
||||
12 1 -0.8686409 0.29553717 1.7585884856051632 0
|
||||
13 0 -5.3742585 0.0046129758 0.0066705145609350898 0
|
||||
14 1 8.055311 0.99968266 0.00045789047280600954 1
|
||||
15 1 0.7239418 0.67347443 0.57030492115977471 1
|
||||
16 0 -4.9659567 0.0069230157 0.010022533659478326 0
|
||||
17 0 -4.53453 0.010617997 0.015400437348225601 0
|
||||
18 1 6.541239 0.99855936 0.0020799089000798346 1
|
||||
19 0 -3.4611526 0.030438 0.044594938950929142 0
|
||||
20 1 6.5971622 0.9986376 0.0019668438274693619 1
|
||||
21 1 6.909395 0.99900264 0.0014396108762453246 1
|
||||
22 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -6.144596 0.0021404508 0.003091327290821727 0
|
||||
25 1 0.99810886 0.7306866 0.45267533493847784 1
|
||||
26 0 -5.5653734 0.003813552 0.0055123099512810001 0
|
||||
27 0 -4.4292684 0.011782723 0.017099815587542869 0
|
||||
28 0 -6.0393343 0.0023774789 0.0034340608007065211 0
|
||||
29 0 -6.4124374 0.0016383312 0.0023655506303733803 0
|
||||
30 0 -5.5759807 0.0037734662 0.0054542582138011643 0
|
||||
31 0 -5.6079073 0.0036553284 0.0052831859608574111 0
|
||||
32 1 6.2956944 0.99815917 0.0026582034961301652 1
|
||||
33 0 -5.28982 0.005017366 0.0072567488938147648 0
|
||||
34 0 -5.205139 0.00545833 0.007896275235753987 0
|
||||
35 0 -6.0393343 0.0023774789 0.0034340608007065211 0
|
||||
36 1 7.9358006 0.99964243 0.00051595421118705818 1
|
||||
37 0 -1.7981725 0.14207368 0.22107433591479689 0
|
||||
38 1 4.925625 0.9927941 0.010433558205305159 1
|
||||
39 1 0.8958168 0.7100891 0.49392805898568881 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 2.7223396 0.9383321 0.091829504133865536 1
|
||||
42 1 6.456028 0.99843144 0.0022647243580549464 1
|
||||
43 1 -0.5776615 0.35947084 1.4760533326638845 0
|
||||
44 1 7.0351353 0.99912035 0.0012696184696093834 1
|
||||
45 0 -6.2041197 0.0020170107 0.0029128699704284991 0
|
||||
46 1 2.9662514 0.95102596 0.072443367961129171 1
|
||||
47 0 -6.576023 0.0013914409 0.0020088227357911541 0
|
||||
48 0 -3.8983564 0.019872293 0.028958355424267362 0
|
||||
49 1 4.0427475 0.98275346 0.025098563305591001 1
|
||||
50 1 2.1004105 0.89094305 0.16659487812744161 1
|
||||
51 1 -0.5968132 0.35507312 1.4938119293851178 0
|
||||
52 1 4.954878 0.9930004 0.010133813210851364 1
|
||||
53 1 7.1512375 0.99921674 0.0011304547879411168 1
|
||||
54 1 6.3703938 0.99829143 0.0024670495040164395 1
|
||||
55 1 3.6747475 0.9752712 0.036124603704880104 1
|
||||
56 1 4.184519 0.98499894 0.021805920782064475 1
|
||||
57 1 1.3889561 0.8004255 0.32116091231787897 1
|
||||
58 1 1.2304935 0.7739049 0.36977176419268548 1
|
||||
59 1 0.9474783 0.72060776 0.47271391197478296 1
|
||||
60 1 1.7859488 0.8564299 0.22359297266463068 1
|
||||
61 0 -6.190854 0.0020438908 0.0029517286074231225 0
|
||||
62 1 4.654825 0.9905741 0.013663163504217138 1
|
||||
63 1 -0.14215279 0.46452153 1.1061826380095336 0
|
||||
64 0 -6.576023 0.0013914409 0.0020088227357911541 0
|
||||
65 1 3.702014 0.9759204 0.035164645125885144 1
|
||||
66 0 -4.53453 0.010617997 0.015400437348225601 0
|
||||
67 1 3.0539417 0.9549524 0.066499243273409794 1
|
||||
68 1 10.140726 0.99996054 5.6927380571090205E-05 1
|
||||
69 0 -5.9195843 0.0026791198 0.0038703397453190326 0
|
||||
70 0 -3.8342557 0.021159997 0.030855032857469784 0
|
||||
71 1 6.7781963 0.998863 0.0016413037970021442 1
|
||||
72 0 -3.2595625 0.03698479 0.054369510681409522 0
|
||||
73 1 7.9999104 0.9996646 0.00048395435075601986 1
|
||||
74 1 1.8288183 0.8616209 0.21487484464489795 1
|
||||
75 0 -4.8054824 0.008118305 0.011760039524949285 0
|
||||
76 0 -5.683504 0.003390086 0.0048991694312840745 0
|
||||
77 0 -4.0899663 0.01646419 0.023950514509151567 0
|
||||
78 0 -4.300881 0.013375288 0.019426671721870053 0
|
||||
79 0 -5.9659986 0.0025579226 0.0036950300971428213 0
|
||||
80 0 -3.647421 0.025396463 0.037112635869023899 0
|
||||
81 0 -4.64787 0.009491046 0.01375807798387328 0
|
||||
82 0 -3.9872336 0.018213091 0.026518165349789534 0
|
||||
83 0 -2.960342 0.04924999 0.072862048549978256 0
|
||||
84 1 8.486448 0.9997938 0.0002974746612633519 1
|
||||
85 1 5.1310043 0.9941241 0.0085021141109656376 1
|
||||
86 1 1.4144373 0.8044649 0.31389866136746908 1
|
||||
87 1 5.359477 0.99531865 0.0067696165596147816 1
|
||||
88 0 -4.53453 0.010617997 0.015400437348225601 0
|
||||
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595 0 -4.4292684 0.011782723 0.017099815587542869 0
|
||||
596 0 -4.668451 0.009299507 0.013479125365326175 0
|
||||
597 0 -3.4028287 0.032207176 0.047229853075843209 0
|
||||
598 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
599 0 -4.0309043 0.01744841 0.025394935742158363 0
|
||||
600 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
601 0 -6.6493583 0.0012931783 0.0018668692759971461 0
|
||||
602 0 -4.9659567 0.0069230157 0.010022533659478326 0
|
||||
603 1 4.077029 0.983325 0.024259765811518532 1
|
||||
604 1 4.763052 0.9915328 0.012267593397173494 1
|
||||
605 1 9.002544 0.9998769 0.00017758301601775289 1
|
||||
606 0 -5.079297 0.0061857807 0.0089519111177552638 0
|
||||
607 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
608 1 8.711157 0.9998353 0.00023761359871469259 1
|
||||
609 0 -4.8606954 0.007685571 0.011130763272308981 0
|
||||
610 1 7.812685 0.9995956 0.00058356915380086715 1
|
||||
611 1 5.3003206 0.99503475 0.0071811783301834005 1
|
||||
612 1 15.400112 0.9999998 2.5797399935888651E-07 1
|
||||
613 0 -6.0181193 0.002428332 0.0035076032324912391 0
|
||||
614 0 -6.11267 0.0022097363 0.003191503044331506 0
|
||||
615 0 -4.3709445 0.0124815395 0.018120376980472964 0
|
||||
616 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
617 0 ? ? ? 0
|
||||
618 0 -4.9659567 0.0069230157 0.010022533659478326 0
|
||||
619 0 -4.4292684 0.011782723 0.017099815587542869 0
|
||||
620 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
621 0 -0.455966 0.38794324 0.70826264061358124 0
|
||||
622 0 -2.4222107 0.08149462 0.12263992620333727 0
|
||||
623 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
624 0 -4.184517 0.015001101 0.021805983529610589 0
|
||||
625 0 -4.1102185 0.016139435 0.023474226408927761 0
|
||||
626 1 4.3904896 0.9877571 0.0177718028993476 1
|
||||
627 0 -4.930086 0.007174042 0.010387258920416115 0
|
||||
628 0 -6.4707613 0.001545654 0.0022316325112485642 0
|
||||
629 0 -5.3973837 0.004507999 0.0065183711781357278 0
|
||||
630 0 -3.7183867 0.023697877 0.034600427151850646 0
|
||||
631 0 -4.4292684 0.011782723 0.017099815587542869 0
|
||||
632 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
633 1 3.4009485 0.96773416 0.047317309321993302 1
|
||||
634 0 -5.9340725 0.0026406858 0.0038147432465966695 0
|
||||
635 0 -5.0415535 0.006422188 0.0092951385862841233 0
|
||||
636 1 8.94829 0.99987006 0.0001874732696668834 1
|
||||
637 0 -3.0414886 0.04558636 0.067313431553847239 0
|
||||
638 0 -5.3973837 0.004507999 0.0065183711781357278 0
|
||||
639 0 -4.2245216 0.014421315 0.020957039422526056 0
|
||||
640 0 -4.76121 0.00848268 0.012290120560278259 0
|
||||
641 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
642 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
643 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
644 0 -6.4707613 0.001545654 0.0022316325112485642 0
|
||||
645 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
646 0 -6.699336 0.0012302141 0.0017759164424290444 0
|
||||
647 0 -6.371276 0.001707057 0.0024648671060441625 0
|
||||
648 1 11.002037 0.9999833 2.4077772041939022E-05 1
|
||||
649 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
650 0 -4.387144 0.012283436 0.017830990738932077 0
|
||||
651 0 -5.929241 0.0026534407 0.0038331935575717608 0
|
||||
652 0 -4.204257 0.014712196 0.021382895210531865 0
|
||||
653 0 -4.9659567 0.0069230157 0.010022533659478326 0
|
||||
654 0 -5.0712185 0.0062356424 0.0090242957807927775 0
|
||||
655 0 -5.5026455 0.004059428 0.0058684357974981846 0
|
||||
656 0 -4.4292684 0.011782723 0.017099815587542869 0
|
||||
657 0 -1.2109241 0.22953758 0.37620349851234491 0
|
||||
658 1 7.3752346 0.9993738 0.00090370784834111395 1
|
||||
659 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
660 0 -6.576023 0.0013914409 0.0020088227357911541 0
|
||||
661 0 -4.53453 0.010617997 0.015400437348225601 0
|
||||
662 0 -5.9810104 0.0025199065 0.0036400448237806026 0
|
||||
663 0 -5.9810104 0.0025199065 0.0036400448237806026 0
|
||||
664 0 -4.8664713 0.0076416456 0.011066902967746477 0
|
||||
665 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
666 0 -3.8625917 0.02058099 0.03000189698203623 0
|
||||
667 0 -5.0712185 0.0062356424 0.0090242957807927775 0
|
||||
668 1 1.7462807 0.85148305 0.23195028869490306 1
|
||||
669 1 6.513283 0.9985186 0.0021388130273243158 1
|
||||
670 1 4.352824 0.9872931 0.018449615023574704 1
|
||||
671 0 -4.742524 0.008641293 0.012520927707100915 0
|
||||
672 0 -5.6079073 0.0036553284 0.0052831859608574111 0
|
||||
673 0 -4.468639 0.011332998 0.016443413675454117 0
|
||||
674 0 -6.576023 0.0013914409 0.0020088227357911541 0
|
||||
675 0 -4.504865 0.010934202 0.015861594843319216 0
|
||||
676 0 -6.278517 0.0018726679 0.0027042215761260723 0
|
||||
677 0 -4.8606954 0.007685571 0.011130763272308981 0
|
||||
678 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
679 0 -6.4707613 0.001545654 0.0022316325112485642 0
|
||||
680 1 15.284445 0.99999976 3.4396534272948301E-07 1
|
||||
681 1 7.982872 0.9996589 0.00049221231130512611 1
|
||||
682 0 -3.77283 0.022470392 0.032787695020670936 0
|
||||
683 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
684 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
685 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
686 0 -7.00745 0.0009042951 0.0013052123339953601 0
|
||||
687 0 -5.052623 0.00635194 0.0091931402933308863 0
|
||||
688 0 -5.3973837 0.004507999 0.0065183711781357278 0
|
||||
689 0 -4.404127 0.012079086 0.017532540698851264 0
|
||||
690 0 -6.371276 0.001707057 0.0024648671060441625 0
|
||||
691 1 2.8413372 0.94486916 0.081813526767298625 1
|
||||
692 0 -5.9340725 0.0026406858 0.0038147432465966695 0
|
||||
693 0 -5.130742 0.005877423 0.0085043455317272768 0
|
||||
694 0 -5.4755683 0.004170383 0.0060291714053851949 0
|
||||
695 0 -6.4707613 0.001545654 0.0022316325112485642 0
|
||||
696 1 5.1025124 0.9939553 0.0087471017414342198 1
|
||||
697 1 3.355402 0.9662813 0.049484860722445317 1
|
||||
698 1 4.2769833 0.98630565 0.019893290833474131 1
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 8 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 8 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 129 | 5 | 0.9627
|
||||
negative || 7 | 213 | 0.9682
|
||||
||======================
|
||||
Precision || 0.9485 | 0.9771 |
|
||||
OVERALL 0/1 ACCURACY: 0.966102
|
||||
LOG LOSS/instance: 0.137058
|
||||
Test-set entropy (prior Log-Loss/instance): 0.956998
|
||||
LOG-LOSS REDUCTION (RIG): 0.856784
|
||||
AUC: 0.994166
|
||||
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 96 | 9 | 0.9143
|
||||
negative || 3 | 221 | 0.9866
|
||||
||======================
|
||||
Precision || 0.9697 | 0.9609 |
|
||||
OVERALL 0/1 ACCURACY: 0.963526
|
||||
LOG LOSS/instance: 0.130675
|
||||
Test-set entropy (prior Log-Loss/instance): 0.903454
|
||||
LOG-LOSS REDUCTION (RIG): 0.855361
|
||||
AUC: 0.997279
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995722 (0.0016)
|
||||
Accuracy: 0.964814 (0.0013)
|
||||
Positive precision: 0.959113 (0.0106)
|
||||
Positive recall: 0.938486 (0.0242)
|
||||
Negative precision: 0.968967 (0.0081)
|
||||
Negative recall: 0.977394 (0.0092)
|
||||
Log-loss: 0.133866 (0.0032)
|
||||
Log-loss reduction: 0.856072 (0.0007)
|
||||
F1 Score: 0.948366 (0.0072)
|
||||
AUPRC: 0.991520 (0.0025)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 337 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.5082830786705017 Improvement: 0.1849
|
||||
[3] (%Time%) 2 iterations Loss: 0.22038933634757996 Improvement: 0.2673
|
||||
[3] (%Time%) 3 iterations Loss: 0.15389013290405273 Improvement: 0.1143
|
||||
[3] (%Time%) 4 iterations Loss: 0.14956159889698029 Improvement: 0.0315
|
||||
[3] (%Time%) 5 iterations Loss: 0.14420396089553833 Improvement: 0.01187
|
||||
[3] (%Time%) 6 iterations Loss: 0.14081034064292908 Improvement: 0.005512
|
||||
[3] (%Time%) 7 iterations Loss: 0.13886460661888123 Improvement: 0.002837
|
||||
[3] (%Time%) 8 iterations Loss: 0.1385388970375061 Improvement: 0.0009535
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Normalize #2' started.
|
||||
[4] (%Time%) 362 examples
|
||||
[4] 'Normalize #2' finished in %Time%.
|
||||
[5] 'LBFGS data prep #2' started.
|
||||
[5] 'LBFGS data prep #2' finished in %Time%.
|
||||
[6] 'LBFGS Optimizer #2' started.
|
||||
[6] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[6] (%Time%) 1 iterations Loss: 0.517149806022644 Improvement: 0.176
|
||||
[6] (%Time%) 2 iterations Loss: 0.22123011946678162 Improvement: 0.2719
|
||||
[6] (%Time%) 3 iterations Loss: 0.17534516751766205 Improvement: 0.09971
|
||||
[6] (%Time%) 4 iterations Loss: 0.1547054648399353 Improvement: 0.04017
|
||||
[6] (%Time%) 5 iterations Loss: 0.14621466398239136 Improvement: 0.01639
|
||||
[6] (%Time%) 6 iterations Loss: 0.14243879914283752 Improvement: 0.006927
|
||||
[6] (%Time%) 7 iterations Loss: 0.141435906291008 Improvement: 0.002484
|
||||
[6] (%Time%) 8 iterations Loss: 0.14130792021751404 Improvement: 0.0007169
|
||||
[6] 'LBFGS Optimizer #2' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995722 0.964814 0.959113 0.938486 0.968967 0.977394 0.133866 0.856072 0.948366 0.99152 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
5 1 8.195853 0.99972427 0.00039785072387288643 1
|
||||
6 0 -0.92675495 0.28358352 0.48112957403187329 0
|
||||
8 0 -4.441124 0.011645473 0.016899458781483138 0
|
||||
9 0 -3.5258589 0.028585356 0.041840859443891751 0
|
||||
10 0 -4.437841 0.011683321 0.016954706359182743 0
|
||||
11 0 -4.2941904 0.013463867 0.019556202484947195 0
|
||||
18 1 5.0770636 0.99380046 0.0089718833435623487 1
|
||||
20 1 3.5104084 0.9709825 0.042482812606117214 1
|
||||
21 1 4.914091 0.9927111 0.010554131975312969 1
|
||||
25 1 0.77733994 0.6851065 0.54559978857802161 1
|
||||
28 0 -4.2941904 0.013463867 0.019556202484947195 0
|
||||
31 0 -4.078273 0.016654618 0.02422986930767649 0
|
||||
32 1 4.2802258 0.9863494 0.019829298264044417 1
|
||||
35 0 -4.2941904 0.013463867 0.019556202484947195 0
|
||||
37 0 -1.9575973 0.123727314 0.19054820534498015 0
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.345798 0.79344183 0.33380363322138745 1
|
||||
44 1 5.221752 0.9946311 0.0077665361772382407 1
|
||||
45 0 -4.559692 0.010356895 0.015019754580703122 0
|
||||
46 1 3.5480871 0.97202545 0.040934001279844333 1
|
||||
48 0 -3.3026972 0.035478774 0.052115107886184495 0
|
||||
50 1 0.88028383 0.70688105 0.50046063566430887 1
|
||||
51 1 -0.46719694 0.3852799 1.3760211950656485 0
|
||||
52 1 3.0009165 0.9526155 0.070034072676330272 1
|
||||
54 1 4.4026546 0.98790336 0.017558180448476325 1
|
||||
56 1 4.634062 0.99037826 0.013948447995498612 1
|
||||
60 1 0.9002085 0.71099234 0.49209408564780061 1
|
||||
63 1 -0.05405426 0.4864897 1.0395187952947689 0
|
||||
64 0 -4.5769377 0.010181617 0.014764258553784819 0
|
||||
66 0 -3.5127785 0.028950822 0.042383732687606798 0
|
||||
68 1 5.402814 0.9955163 0.0064831566138852277 1
|
||||
69 0 -4.209711 0.014633344 0.021267441377494655 0
|
||||
70 0 -3.0025942 0.047308814 0.069919452955735761 0
|
||||
71 1 2.9094105 0.94830966 0.076569862833575114 1
|
||||
72 0 -2.3919716 0.08378696 0.12624499395390246 0
|
||||
73 1 5.358176 0.9953126 0.0067784289554959475 1
|
||||
74 1 1.2702999 0.7807941 0.35698597297037243 1
|
||||
76 0 -3.9119062 0.019610088 0.028572456094766233 0
|
||||
77 0 -2.935234 0.05043905 0.074667486276515149 0
|
||||
79 0 -4.21737 0.014523317 0.021106358444838338 0
|
||||
82 0 -3.0271943 0.046212338 0.06825997430473224 0
|
||||
88 0 -3.5127785 0.028950822 0.042383732687606798 0
|
||||
90 0 -4.3610206 0.012604453 0.018299955640704732 0
|
||||
91 0 -4.12232 0.015948394 0.023194119449028192 0
|
||||
92 0 -3.5127785 0.028950822 0.042383732687606798 0
|
||||
93 0 -4.5769377 0.010181617 0.014764258553784819 0
|
||||
95 0 -4.3610206 0.012604453 0.018299955640704732 0
|
||||
96 0 -4.4050674 0.01206787 0.017516161799628286 0
|
||||
97 0 -3.2300313 0.038051102 0.055967840193562365 0
|
||||
98 1 4.465864 0.9886359 0.016488802812084358 1
|
||||
99 1 5.9911737 0.9975055 0.0036033190964105239 1
|
||||
100 1 2.996025 0.95239425 0.070369188863546026 1
|
||||
102 0 -3.2911012 0.035877734 0.052711980841894067 0
|
||||
104 1 5.82374 0.9970522 0.0042590675344212885 1
|
||||
105 1 -0.18393278 0.454146 1.1387719286748768 0
|
||||
106 1 6.389801 0.9983242 0.002419674107522367 1
|
||||
108 0 -4.2743187 0.013730383 0.019946003466210564 0
|
||||
109 1 3.9310474 0.98075455 0.028035965212125667 1
|
||||
111 1 2.7906513 0.94216853 0.085942944675007849 1
|
||||
112 1 4.613146 0.9901769 0.014241778574439458 1
|
||||
113 1 6.5796623 0.9986136 0.0020015460261031213 1
|
||||
115 0 -3.2665153 0.03673795 0.053999764373317827 0
|
||||
117 1 4.30818 0.9867207 0.019286347482424176 1
|
||||
120 0 -4.0343027 0.017390244 0.025309531268978596 0
|
||||
121 0 -2.9283564 0.050769474 0.075169598985582772 0
|
||||
122 1 6.92715 0.99902016 0.0014143044085270927 1
|
||||
123 1 2.6919937 0.9365526 0.094568095834527757 1
|
||||
125 0 -4.5769377 0.010181617 0.014764258553784819 0
|
||||
128 1 3.2324018 0.9620356 0.055837818607870147 1
|
||||
129 0 -4.1782913 0.015093369 0.021941131707930758 0
|
||||
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463 0 -3.7123485 0.02383798 0.034807473512979727 0
|
||||
468 0 -3.8899307 0.02003707 0.029200918717211664 0
|
||||
469 0 -4.653878 0.00943473 0.013676054784740255 0
|
||||
470 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
471 0 -2.876761 0.05331438 0.079042687429972211 0
|
||||
472 0 -3.1583672 0.040762853 0.060040565689142664 0
|
||||
473 0 -3.8899307 0.02003707 0.029200918717211664 0
|
||||
475 0 -4.2557125 0.013984637 0.020317970121872225 0
|
||||
476 0 -3.6719763 0.02479571 0.03622362117060713 0
|
||||
477 0 -3.8899307 0.02003707 0.029200918717211664 0
|
||||
478 0 -3.3222516 0.03481567 0.051123600239514386 0
|
||||
479 1 4.300199 0.9866157 0.019439824329387352 1
|
||||
481 0 -2.4799905 0.07727288 0.11602403059173376 0
|
||||
485 0 -4.0836906 0.016566122 0.024100039859206706 0
|
||||
486 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
488 1 0.060478687 0.5151151 0.95703331295302563 1
|
||||
490 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
491 1 3.546741 0.97198886 0.040988320503243865 1
|
||||
494 0 -0.6795654 0.3363583 0.5915235729553544 0
|
||||
496 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
498 0 -3.7744875 0.022434015 0.032734008049047275 0
|
||||
499 0 -3.7744875 0.022434015 0.032734008049047275 0
|
||||
500 0 -2.9274807 0.050811693 0.075233766839019209 0
|
||||
503 0 -3.6590443 0.025110347 0.036689164602033801 0
|
||||
505 0 -3.8519175 0.020797258 0.03032049732070655 0
|
||||
506 1 5.452454 0.9957325 0.0061698953672870557 1
|
||||
508 0 -3.524149 0.028632876 0.041911435136248953 0
|
||||
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|
||||
511 0 -3.4087057 0.032024495 0.046957555342256867 0
|
||||
512 0 -3.524149 0.028632876 0.041911435136248953 0
|
||||
515 1 4.7465196 0.99139285 0.01247123959247701 1
|
||||
516 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
518 0 -3.8969548 0.019899612 0.028998568689929841 0
|
||||
524 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
525 0 -4.114909 0.01606512 0.02336525941995251 0
|
||||
526 0 -3.8899307 0.02003707 0.029200918717211664 0
|
||||
530 1 3.5453553 0.97195107 0.041044411228734458 1
|
||||
536 0 -3.2932625 0.03580305 0.052600228461958214 0
|
||||
537 0 -3.075308 0.04413734 0.065124750768219858 0
|
||||
542 0 -3.1203542 0.04227543 0.062317279067489628 0
|
||||
543 0 -3.7744875 0.022434015 0.032734008049047275 0
|
||||
545 0 -3.4087057 0.032024495 0.046957555342256867 0
|
||||
550 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
551 0 -4.756389 0.008523323 0.012349258747389995 0
|
||||
552 0 -3.1711454 0.040266126 0.059293681195191732 0
|
||||
553 0 -1.7068839 0.15356833 0.24053448231112076 0
|
||||
554 0 -4.2557125 0.013984637 0.020317970121872225 0
|
||||
555 0 -1.8967295 0.13047908 0.20170736090262009 0
|
||||
556 0 -2.8480508 0.05478216 0.081281235385693928 0
|
||||
562 0 -4.756389 0.008523323 0.012349258747389995 0
|
||||
564 0 -3.481462 0.029844321 0.043711822997860618 0
|
||||
567 0 -3.152509 0.040992532 0.060386044907582849 0
|
||||
568 1 2.4574275 0.92110294 0.11856569620007711 1
|
||||
570 1 4.2511344 0.9859521 0.02041056658227668 1
|
||||
571 1 6.0623946 0.9976766 0.0033558418397778241 1
|
||||
572 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
573 0 -4.871833 0.007601095 0.011007951358500074 0
|
||||
574 1 3.5784144 0.9728384 0.03972791608026132 1
|
||||
575 0 -3.075308 0.04413734 0.065124750768219858 0
|
||||
576 0 -3.4087057 0.032024495 0.046957555342256867 0
|
||||
579 0 -4.756389 0.008523323 0.012349258747389995 0
|
||||
580 0 -3.1907513 0.039515253 0.058165391801205106 0
|
||||
583 0 -4.2557125 0.013984637 0.020317970121872225 0
|
||||
585 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
587 0 -3.2311234 0.038011145 0.055907914725901897 0
|
||||
588 1 2.972674 0.9513242 0.071990979542995756 1
|
||||
589 0 -3.524149 0.028632876 0.041911435136248953 0
|
||||
591 1 2.577313 0.92938715 0.10564839365041979 1
|
||||
592 1 3.0708642 0.95567477 0.065408367010338489 1
|
||||
595 0 -3.4087057 0.032024495 0.046957555342256867 0
|
||||
596 0 -3.556533 0.027745793 0.040594522058891185 0
|
||||
597 0 -2.8573537 0.05430244 0.080549219084807347 0
|
||||
598 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
599 0 -2.6683884 0.06486466 0.096752913456643053 0
|
||||
601 0 -4.871833 0.007601095 0.011007951358500074 0
|
||||
603 1 1.5632815 0.8268237 0.2743483323675347 1
|
||||
605 1 5.468206 0.99579895 0.0060736073284776764 1
|
||||
608 1 5.3596387 0.9953194 0.0067684934149875595 1
|
||||
610 1 4.1051517 0.9837799 0.023592505205891071 1
|
||||
611 1 3.3511672 0.966143 0.049691336986102508 1
|
||||
615 0 -3.3385787 0.034271166 0.05030994140635453 0
|
||||
616 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
620 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
623 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
625 0 -2.8868356 0.052808177 0.0782714680728607 0
|
||||
626 1 2.0856414 0.8894997 0.1689339371879359 1
|
||||
628 0 -4.6214943 0.0097422395 0.01412399251641484 0
|
||||
630 0 -2.520561 0.07442929 0.1115848819670567 0
|
||||
631 0 -3.4087057 0.032024495 0.046957555342256867 0
|
||||
632 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
635 0 -3.601849 0.026549164 0.038819977520619012 0
|
||||
636 1 5.0524154 0.99364674 0.00919505569096731 1
|
||||
637 0 -2.3261313 0.08898178 0.1344481831772191 0
|
||||
640 0 -3.6083245 0.026382321 0.038572730795762511 0
|
||||
643 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
646 0 -4.567424 0.010277945 0.014904666281369309 0
|
||||
647 0 -4.7056694 0.0089628 0.01298888253711963 0
|
||||
648 1 4.4699655 0.98868185 0.016421742966084056 1
|
||||
650 0 -3.1819413 0.039850987 0.058669769344754465 0
|
||||
651 0 -4.3883286 0.012269074 0.01781001333640217 0
|
||||
655 0 -4.1402693 0.015669134 0.022784759806126029 0
|
||||
658 1 4.660341 0.9906255 0.013588335585102262 1
|
||||
659 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
662 0 -4.4359236 0.0117054805 0.016987054615104656 0
|
||||
663 0 -4.4359236 0.0117054805 0.016987054615104656 0
|
||||
664 0 -3.8586626 0.02066034 0.030118785949610843 0
|
||||
666 0 -2.963494 0.049102604 0.072638416082721383 0
|
||||
667 0 -4.024826 0.017552922 0.025548399182159842 0
|
||||
669 1 4.774997 0.9916325 0.012122508682504231 1
|
||||
671 0 -3.6821253 0.024551477 0.035862355298402988 0
|
||||
672 0 -4.390608 0.012241483 0.017769713529052416 0
|
||||
673 0 -2.9023998 0.05203506 0.077094391782711852 0
|
||||
674 0 -4.871833 0.007601095 0.011007951358500074 0
|
||||
675 0 -3.2360675 0.03783077 0.055637431882624926 0
|
||||
676 0 -4.653878 0.00943473 0.013676054784740255 0
|
||||
677 0 -3.524149 0.028632876 0.041911435136248953 0
|
||||
684 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
686 0 -4.987276 0.0067779734 0.0098118384640706255 0
|
||||
687 0 -3.7851043 0.022202358 0.03239216813998242 0
|
||||
690 0 -4.7056694 0.0089628 0.01298888253711963 0
|
||||
695 0 -4.6214943 0.0097422395 0.01412399251641484 0
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Warning: Skipped 16 instances with missing features/label/weight during training
|
||||
Beginning optimization
|
||||
num vars: 10
|
||||
improvement criterion: Mean Improvement
|
||||
L1 regularization selected 10 of 10 weights.
|
||||
Not training a calibrator because it is not needed.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 228 | 11 | 0.9540
|
||||
negative || 9 | 435 | 0.9797
|
||||
||======================
|
||||
Precision || 0.9620 | 0.9753 |
|
||||
OVERALL 0/1 ACCURACY: 0.970717
|
||||
LOG LOSS/instance: 0.119042
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.872546
|
||||
AUC: 0.996108
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996108 (0.0000)
|
||||
Accuracy: 0.970717 (0.0000)
|
||||
Positive precision: 0.962025 (0.0000)
|
||||
Positive recall: 0.953975 (0.0000)
|
||||
Negative precision: 0.975336 (0.0000)
|
||||
Negative recall: 0.979730 (0.0000)
|
||||
Log-loss: 0.119042 (0.0000)
|
||||
Log-loss reduction: 0.872546 (0.0000)
|
||||
F1 Score: 0.957983 (0.0000)
|
||||
AUPRC: 0.992030 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
--- Progress log ---
|
||||
[1] 'Normalize' started.
|
||||
[1] (%Time%) 699 examples
|
||||
[1] 'Normalize' finished in %Time%.
|
||||
[2] 'LBFGS data prep' started.
|
||||
[2] 'LBFGS data prep' finished in %Time%.
|
||||
[3] 'LBFGS Optimizer' started.
|
||||
[3] (%Time%) 0 iterations Loss: 0.6931471824645996
|
||||
[3] (%Time%) 1 iterations Loss: 0.503106415271759 Improvement: 0.19
|
||||
[3] (%Time%) 2 iterations Loss: 0.42320528626441956 Improvement: 0.1019
|
||||
[3] (%Time%) 3 iterations Loss: 0.16024325788021088 Improvement: 0.2246
|
||||
[3] (%Time%) 4 iterations Loss: 0.13900212943553925 Improvement: 0.07149
|
||||
[3] (%Time%) 5 iterations Loss: 0.12618772685527802 Improvement: 0.02744
|
||||
[3] (%Time%) 6 iterations Loss: 0.12205933779478073 Improvement: 0.009952
|
||||
[3] (%Time%) 7 iterations Loss: 0.11975764483213425 Improvement: 0.004214
|
||||
[3] (%Time%) 8 iterations Loss: 0.11760365962982178 Improvement: 0.002669
|
||||
[3] (%Time%) 9 iterations Loss: 0.11399238556623459 Improvement: 0.003376
|
||||
[3] (%Time%) 10 iterations Loss: 0.11343003809452057 Improvement: 0.001266
|
||||
[3] (%Time%) 11 iterations Loss: 0.11329112946987152 Improvement: 0.0004206
|
||||
[3] 'LBFGS Optimizer' finished in %Time%.
|
||||
[4] 'Saving model' started.
|
||||
[4] 'Saving model' finished in %Time%.
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
LogisticRegression
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.996108 0.970717 0.962025 0.953975 0.975336 0.97973 0.119042 0.872546 0.957983 0.99203 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.4762254 0.029996313 0.043937864099631886 0
|
||||
1 0 1.9774284 0.87840676 3.0398651072956344 1
|
||||
2 0 -3.93716 0.019130416 0.027866766122312772 0
|
||||
3 0 1.9661341 0.87719524 3.0255616022478864 1
|
||||
4 0 -3.5451653 0.028054101 0.041052083508060465 0
|
||||
5 1 8.377454 0.99977005 0.00033179268362335749 1
|
||||
6 0 -1.9164152 0.12826185 0.19803324588790117 0
|
||||
7 0 -4.4696636 0.011321522 0.01642666797517562 0
|
||||
8 0 -4.810614 0.008077087 0.011700088369940806 0
|
||||
9 0 -3.9204557 0.019446393 0.028331590677166323 0
|
||||
10 0 -5.141514 0.005814819 0.0084134959784060125 0
|
||||
11 0 -4.9192867 0.0072513726 0.010499633786939466 0
|
||||
12 1 -0.85985374 0.2973699 1.7496694835410176 0
|
||||
13 0 -4.3980947 0.012151285 0.017637979128819783 0
|
||||
14 1 5.6814957 0.99660313 0.0049089879584418182 1
|
||||
15 1 0.25190878 0.56264627 0.82969989583644721 1
|
||||
16 0 -4.1038265 0.016241249 0.023623530310215374 0
|
||||
17 0 -3.8839555 0.020154733 0.029374152252344635 0
|
||||
18 1 5.449606 0.9957204 0.0061874265268757394 1
|
||||
19 0 -3.0684948 0.04442568 0.065560013263418176 0
|
||||
20 1 3.9626808 0.9813426 0.027171185979008489 1
|
||||
21 1 5.2650557 0.9948575 0.0074382155674881083 1
|
||||
22 0 -4.511556 0.010862078 0.015756394824142473 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.107146 0.0060169133 0.0087067913827365544 0
|
||||
25 1 0.67267895 0.66210276 0.59487295303894816 1
|
||||
26 0 -4.3625536 0.012585388 0.018272099665311091 0
|
||||
27 0 -3.6960962 0.024219107 0.035370860931637681 0
|
||||
28 0 -4.9192867 0.0072513726 0.010499633786939466 0
|
||||
29 0 -5.0873837 0.0061362656 0.0088800329967917858 0
|
||||
30 0 -4.545924 0.010498964 0.015226877455174592 0
|
||||
31 0 -4.6994157 0.009018519 0.013069997580773615 0
|
||||
32 1 4.570427 0.98975253 0.014860243285211676 1
|
||||
33 0 -4.5300207 0.010665474 0.015469670227781259 0
|
||||
34 0 -4.281804 0.013629384 0.019798272915656116 0
|
||||
35 0 -4.9192867 0.0072513726 0.010499633786939466 0
|
||||
36 1 6.09622 0.9977537 0.0032444004285129453 1
|
||||
37 0 -2.040009 0.11506581 0.17635792976232192 0
|
||||
38 1 3.6194649 0.97390234 0.038150977482211336 1
|
||||
39 1 0.67302036 0.6621791 0.59470659134453041 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.7537603 0.85242647 0.23035270287832674 1
|
||||
42 1 5.6079683 0.99634486 0.0052829068123115145 1
|
||||
43 1 -0.51844835 0.37321514 1.4219205867507978 0
|
||||
44 1 5.661515 0.9965348 0.0050078732952072418 1
|
||||
45 0 -5.2448807 0.005246777 0.0075894267563155812 0
|
||||
46 1 3.039011 0.9543057 0.06747659243674714 1
|
||||
47 0 -5.327017 0.004835051 0.0069924221413600806 0
|
||||
48 0 -3.5451653 0.028054101 0.041052083508060465 0
|
||||
49 1 4.022252 0.9824026 0.025613682641348189 1
|
||||
50 1 1.3141642 0.78820914 0.34334961540640535 1
|
||||
51 1 -0.5558038 0.36451894 1.4559343157766462 0
|
||||
52 1 3.5690403 0.9725896 0.040096912302954736 1
|
||||
53 1 3.8392248 0.9789427 0.030703688565702728 1
|
||||
54 1 4.629259 0.99033237 0.014015306142000807 1
|
||||
55 1 3.3243551 0.96525496 0.051018028703511277 1
|
||||
56 1 4.665167 0.9906702 0.013523233244319009 1
|
||||
57 1 -0.068742275 0.4828212 1.050439081695961 0
|
||||
58 1 0.9345913 0.71800584 0.47793252444845352 1
|
||||
59 1 0.77084637 0.68370396 0.54855631561229901 1
|
||||
60 1 1.0176353 0.73451173 0.44514255838051037 1
|
||||
61 0 -4.9724913 0.0068782344 0.0099574790561598112 0
|
||||
62 1 4.9776936 0.9931572 0.0099059932451818454 1
|
||||
63 1 -0.16176414 0.4596469 1.1214020514949994 0
|
||||
64 0 -5.327017 0.004835051 0.0069924221413600806 0
|
||||
65 1 1.0385513 0.7385704 0.43719266605733142 1
|
||||
66 0 -3.8839555 0.020154733 0.029374152252344635 0
|
||||
67 1 2.5896845 0.93019474 0.10439531998992779 1
|
||||
68 1 6.0331078 0.9976077 0.0034554827493919762 1
|
||||
69 0 -4.9330077 0.007153263 0.0103570645645221 0
|
||||
70 0 -3.2365923 0.037811674 0.055608799500037949 0
|
||||
71 1 3.4644966 0.9696605 0.044448348657250301 1
|
||||
72 0 -3.0578852 0.044878267 0.066243474030963478 0
|
||||
73 1 5.915409 0.9973097 0.003886534813925873 1
|
||||
74 1 1.604887 0.8327003 0.26413073013227062 1
|
||||
75 0 -4.1288595 0.01584609 0.02304414185758942 0
|
||||
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582 1 5.664747 0.996546 0.0049917370925026622 1
|
||||
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|
||||
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|
||||
585 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
586 1 9.42416 0.99991924 0.00011652295146983004 1
|
||||
587 0 -3.5219579 0.028693879 0.042002041659590345 0
|
||||
588 1 3.4681044 0.9697665 0.044290680876676813 1
|
||||
589 0 -3.9159667 0.019532174 0.028457805466519128 0
|
||||
590 1 1.6600523 0.840245 0.25111802775449782 1
|
||||
591 1 3.2691917 0.9633567 0.053858053688732054 1
|
||||
592 1 3.7409716 0.9768191 0.033836687851945713 1
|
||||
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|
||||
594 1 3.0261364 0.953741 0.068330535719533111 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
602 0 -4.1038265 0.016241249 0.023623530310215374 0
|
||||
603 1 2.1437435 0.8950827 0.15990709080220522 1
|
||||
604 1 2.7813559 0.94166 0.086721866958338889 1
|
||||
605 1 6.549967 0.9985719 0.0020618247821374454 1
|
||||
606 0 -4.1543016 0.015454169 0.022469729701037357 0
|
||||
607 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
608 1 6.7711983 0.998855 0.0016528397973215217 1
|
||||
609 0 -3.9159667 0.019532174 0.028457805466519128 0
|
||||
610 1 5.283313 0.99495006 0.007303986979652617 1
|
||||
611 1 4.019965 0.98236305 0.025671804829685035 1
|
||||
612 1 10.163382 0.99996144 5.5637460368232437E-05 1
|
||||
613 0 -4.5525455 0.010430399 0.015126913120028783 0
|
||||
614 0 -4.9536543 0.007008111 0.010146161522274889 0
|
||||
615 0 -3.6443226 0.025473261 0.037226324663455601 0
|
||||
616 0 -4.511556 0.010862078 0.015756394824142473 0
|
||||
617 0 ? ? ? 0
|
||||
618 0 -4.1038265 0.016241249 0.023623530310215374 0
|
||||
619 0 -3.6960962 0.024219107 0.035370860931637681 0
|
||||
620 0 -4.511556 0.010862078 0.015756394824142473 0
|
||||
621 0 -0.8549547 0.29839453 0.51126810130694178 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
635 0 -4.0421715 0.017256293 0.02511287516157994 0
|
||||
636 1 6.2194853 0.9980137 0.0028685107610920127 1
|
||||
637 0 -2.5378842 0.07324466 0.10973957365564754 0
|
||||
638 0 -4.323697 0.013077516 0.018991319726863218 0
|
||||
639 0 -3.5771766 0.02719431 0.039776427639483594 0
|
||||
640 0 -3.9849067 0.018254748 0.026579378696598457 0
|
||||
641 0 -4.511556 0.010862078 0.015756394824142473 0
|
||||
642 0 -4.511556 0.010862078 0.015756394824142473 0
|
||||
643 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
644 0 -5.1391573 0.005828458 0.0084332877609673198 0
|
||||
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|
||||
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|
||||
647 0 -5.2080975 0.005442294 0.0078730134260600656 0
|
||||
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|
||||
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|
||||
650 0 -3.464087 0.03035152 0.044466262509791271 0
|
||||
651 0 -4.8048563 0.0081233485 0.011767374765468078 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
657 0 -0.93020916 0.28288227 0.47971811412963117 0
|
||||
658 1 5.6904516 0.9966333 0.0048653287013867104 1
|
||||
659 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
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|
||||
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|
||||
662 0 -4.8675127 0.0076337527 0.011055428194332077 0
|
||||
663 0 -4.8675127 0.0076337527 0.011055428194332077 0
|
||||
664 0 -4.1727657 0.015175731 0.022061780213619962 0
|
||||
665 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
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|
||||
667 0 -4.291685 0.013497184 0.019604925764043539 0
|
||||
668 1 1.0617218 0.74301946 0.4285280956552025 1
|
||||
669 1 5.3955727 0.9954839 0.0065301473491096822 1
|
||||
670 1 3.909605 0.9803456 0.028637654512903715 1
|
||||
671 0 -3.93892 0.019097418 0.027818231487795199 0
|
||||
672 0 -4.6994157 0.009018519 0.013069997580773615 0
|
||||
673 0 -3.2851062 0.036085684 0.05302318615310405 0
|
||||
674 0 -5.327017 0.004835051 0.0069924221413600806 0
|
||||
675 0 -3.6344416 0.025719704 0.037591206234064735 0
|
||||
676 0 -5.097265 0.006076298 0.0087929868049173403 0
|
||||
677 0 -3.9159667 0.019532174 0.028457805466519128 0
|
||||
678 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
679 0 -5.1391573 0.005828458 0.0084332877609673198 0
|
||||
680 1 10.698069 0.9999774 3.2591080458755327E-05 1
|
||||
681 1 6.5006 0.9984997 0.002166112977923379 1
|
||||
682 0 -3.3020873 0.03549965 0.052146334557913607 0
|
||||
683 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
684 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
685 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
686 0 -5.5468874 0.0038844286 0.0056149584716269612 0
|
||||
687 0 -4.1632795 0.01531816 0.022270443335792051 0
|
||||
688 0 -4.323697 0.013077516 0.018991319726863218 0
|
||||
689 0 -4.971937 0.0068820207 0.009962979363563133 0
|
||||
690 0 -5.2080975 0.005442294 0.0078730134260600656 0
|
||||
691 1 3.1485786 0.9588527 0.060618878463704597 1
|
||||
692 0 -4.731427 0.008736877 0.0126600342550751 0
|
||||
693 0 -4.4294205 0.011780951 0.01709722956474144 0
|
||||
694 0 -4.3425336 0.012836619 0.018639216844451963 0
|
||||
695 0 -5.1391573 0.005828458 0.0084332877609673198 0
|
||||
696 1 4.210288 0.985375 0.02125524431312269 1
|
||||
697 1 2.564313 0.9285292 0.10698081017479168 1
|
||||
698 1 3.2338276 0.96208763 0.055759787818528549 1
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Training 20 learners for the batch 1
|
||||
Beginning training model 1 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 1 of 20 finished in %Time%
|
||||
Beginning training model 2 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 2 of 20 finished in %Time%
|
||||
Beginning training model 3 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 3 of 20 finished in %Time%
|
||||
Beginning training model 4 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 4 of 20 finished in %Time%
|
||||
Beginning training model 5 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 5 of 20 finished in %Time%
|
||||
Beginning training model 6 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 6 of 20 finished in %Time%
|
||||
Beginning training model 7 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 7 of 20 finished in %Time%
|
||||
Beginning training model 8 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 8 of 20 finished in %Time%
|
||||
Beginning training model 9 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 9 of 20 finished in %Time%
|
||||
Beginning training model 10 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 10 of 20 finished in %Time%
|
||||
Beginning training model 11 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 11 of 20 finished in %Time%
|
||||
Beginning training model 12 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 12 of 20 finished in %Time%
|
||||
Beginning training model 13 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 13 of 20 finished in %Time%
|
||||
Beginning training model 14 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 14 of 20 finished in %Time%
|
||||
Beginning training model 15 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 15 of 20 finished in %Time%
|
||||
Beginning training model 16 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 16 of 20 finished in %Time%
|
||||
Beginning training model 17 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 17 of 20 finished in %Time%
|
||||
Beginning training model 18 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 18 of 20 finished in %Time%
|
||||
Beginning training model 19 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 19 of 20 finished in %Time%
|
||||
Beginning training model 20 of 20
|
||||
Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)
|
||||
Trainer 20 of 20 finished in %Time%
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 232 | 7 | 0.9707
|
||||
negative || 11 | 433 | 0.9752
|
||||
||======================
|
||||
Precision || 0.9547 | 0.9841 |
|
||||
OVERALL 0/1 ACCURACY: 0.973646
|
||||
LOG LOSS/instance: 0.117326
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.874384
|
||||
AUC: 0.995995
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995995 (0.0000)
|
||||
Accuracy: 0.973646 (0.0000)
|
||||
Positive precision: 0.954733 (0.0000)
|
||||
Positive recall: 0.970711 (0.0000)
|
||||
Negative precision: 0.984091 (0.0000)
|
||||
Negative recall: 0.975225 (0.0000)
|
||||
Log-loss: 0.117326 (0.0000)
|
||||
Log-loss reduction: 0.874384 (0.0000)
|
||||
F1 Score: 0.962656 (0.0000)
|
||||
AUPRC: 0.991908 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
WeightedEnsemble
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.995995 0.973646 0.954733 0.970711 0.984091 0.975225 0.117326 0.874384 0.962656 0.991908 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 AllInstanceSelector WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20;/st:AllInstanceSelector
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -2.7285433 0.015308122 0.022255736653645435 0
|
||||
1 0 2.2026808 0.91755587 3.6004393879063104 1
|
||||
2 0 -2.865943 0.0127788205 0.018554748946255879 0
|
||||
3 0 2.3559182 0.93174857 3.8729968952719278 1
|
||||
4 0 -2.5510297 0.019316088 0.028139884703690179 0
|
||||
5 1 7.342853 0.999905 0.00013707668465523314 1
|
||||
6 0 -0.8348112 0.16253074 0.25589185038853235 0
|
||||
7 0 -3.177886 0.008468152 0.012268982363308284 0
|
||||
8 0 -3.238492 0.00781605 0.011320475424983812 0
|
||||
9 0 -2.865943 0.0127788205 0.018554748946255879 0
|
||||
10 0 -3.6651106 0.0044409754 0.0064212420166952023 0
|
||||
11 0 -3.55088 0.005167651 0.0074746743879873997 0
|
||||
12 1 -0.26072478 0.2943738 1.764278770787246 0
|
||||
13 0 -2.962865 0.011247408 0.016318522775694195 0
|
||||
14 1 5.1335754 0.9981972 0.0026032408987607877 1
|
||||
15 1 0.79534745 0.63031197 0.66586204354368062 1
|
||||
16 0 -3.0731657 0.009724431 0.014098047388083948 0
|
||||
17 0 -2.966214 0.011197871 0.016246244772614025 0
|
||||
18 1 4.686339 0.99673235 0.0047219354575893813 1
|
||||
19 0 -2.4672728 0.021548603 0.031427905414005732 0
|
||||
20 1 3.7524228 0.98874325 0.016332160745959907 1
|
||||
21 1 4.512779 0.99588525 0.0059485720047010244 1
|
||||
22 0 -3.3154407 0.00705944 0.010220738044149368 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -3.5781584 0.0049840333 0.0072084185750815781 0
|
||||
25 1 0.99629617 0.69028 0.53474636798823416 1
|
||||
26 0 -3.1513944 0.008769874 0.012708060248567363 0
|
||||
27 0 -2.835495 0.013301122 0.019318226519980827 0
|
||||
28 0 -3.55088 0.005167651 0.0074746743879873997 0
|
||||
29 0 -3.5508797 0.005167653 0.0074746770891751186 0
|
||||
30 0 -3.396992 0.006336849 0.0091712300290694004 0
|
||||
31 0 -3.3802261 0.006479142 0.0093778387844477163 0
|
||||
32 1 4.239458 0.9940872 0.0085556583489895793 1
|
||||
33 0 -3.1767688 0.008480666 0.012287190816689031 0
|
||||
34 0 -3.1104498 0.009257292 0.013417651631193397 0
|
||||
35 0 -3.55088 0.005167651 0.0074746743879873997 0
|
||||
36 1 5.481853 0.998866 0.0016369132539510332 1
|
||||
37 0 -1.6649096 0.060308706 0.089741212336825762 0
|
||||
38 1 3.453434 0.9833229 0.024262826548882763 1
|
||||
39 1 1.5258646 0.81867063 0.28864495484536951 1
|
||||
40 0 ? ? ? 0
|
||||
41 1 1.3881941 0.78982085 0.34040264194884468 1
|
||||
42 1 5.4586825 0.9988305 0.0016882231796356319 1
|
||||
43 1 0.58049154 0.5614732 0.83271095640340953 1
|
||||
44 1 5.2970924 0.99854976 0.0020937735440191363 1
|
||||
45 0 -3.678948 0.0043601627 0.0063041385809690046 0
|
||||
46 1 3.468822 0.983656 0.023774240190126871 1
|
||||
47 0 -3.7710454 0.003858364 0.0055772089261896226 0
|
||||
48 0 -2.5510297 0.019316088 0.028139884703690179 0
|
||||
49 1 3.9899042 0.9917727 0.011918564884792524 1
|
||||
50 1 1.4750896 0.8084053 0.30684935015105891 1
|
||||
51 1 0.031583548 0.3811723 1.3914848155971968 1
|
||||
52 1 2.9732823 0.9688362 0.045675340786728726 1
|
||||
53 1 4.0950465 0.992841 0.010365393442236737 1
|
||||
54 1 3.9028797 0.9907701 0.013377761830287676 1
|
||||
55 1 2.9976287 0.9698013 0.04423889724328689 1
|
||||
56 1 4.527055 0.9959625 0.0058366711251104444 1
|
||||
57 1 0.0076406 0.3736728 1.4201524916218777 1
|
||||
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|
||||
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|
||||
565 1 7.007144 0.9998514 0.00021439230344064893 1
|
||||
566 0 -2.9758658 0.0110563 0.016039703738490549 0
|
||||
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|
||||
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|
||||
569 1 5.680218 0.99912924 0.0012567945385539854 1
|
||||
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|
||||
571 1 6.432602 0.99968046 0.00046107316533662714 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
579 0 -3.5781584 0.0049840333 0.0072084185750815781 0
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||||
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|
||||
581 1 4.957235 0.99772054 0.0032923200422882576 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
586 1 8.320274 0.9999742 3.7234724405850826E-05 1
|
||||
587 0 -2.7196305 0.015488252 0.022519673303817402 0
|
||||
588 1 3.2173567 0.9772949 0.033134099612461547 1
|
||||
589 0 -2.966979 0.011186584 0.016229777200161624 0
|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
605 1 5.6268606 0.99906516 0.0013493187444396134 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
610 1 4.45461 0.995555 0.0064270979778478406 1
|
||||
611 1 4.07425 0.9926412 0.01065574398933648 1
|
||||
612 1 9.368401 0.9999936 9.2011011621090467E-06 1
|
||||
613 0 -3.040134 0.010157744 0.014729463833134492 0
|
||||
614 0 -3.619751 0.004716484 0.0068205450675308749 0
|
||||
615 0 -2.6631837 0.016678462 0.024264851558154606 0
|
||||
616 0 -3.3154407 0.00705944 0.010220738044149368 0
|
||||
617 0 ? ? ? 0
|
||||
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|
||||
619 0 -2.835495 0.013301122 0.019318226519980827 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
636 1 5.7041035 0.99915653 0.0012173767259136749 1
|
||||
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|
||||
638 0 -3.208489 0.008132398 0.011780537653460519 0
|
||||
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|
||||
640 0 -2.8610706 0.012861018 0.018674875021524793 0
|
||||
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|
||||
642 0 -3.3154407 0.00705944 0.010220738044149368 0
|
||||
643 0 -3.9608617 0.002998382 0.0043322490856511197 0
|
||||
644 0 -3.7254226 0.0040993094 0.0059262084034719161 0
|
||||
645 0 -3.3154407 0.00705944 0.010220738044149368 0
|
||||
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|
||||
647 0 -3.571877 0.005025731 0.0072688781465224438 0
|
||||
648 1 6.552003 0.9997274 0.00039329193382559838 1
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
668 1 1.7102344 0.8523484 0.23048485949239214 1
|
||||
669 1 5.366621 0.99867797 0.0019085494570210317 1
|
||||
670 1 4.0897365 0.9927905 0.010438755142753803 1
|
||||
671 0 -2.720616 0.01546823 0.022490332797116083 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
679 0 -3.7254226 0.0040993094 0.0059262084034719161 0
|
||||
680 1 9.424847 0.9999941 8.5131663352343685E-06 1
|
||||
681 1 6.1010246 0.9995029 0.00071734596619533813 1
|
||||
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|
||||
683 0 -3.9608617 0.002998382 0.0043322490856511197 0
|
||||
684 0 -3.9608617 0.002998382 0.0043322490856511197 0
|
||||
685 0 -3.9608617 0.002998382 0.0043322490856511197 0
|
||||
686 0 -3.9608617 0.002998382 0.0043322490856511197 0
|
||||
687 0 -3.0104992 0.010562727 0.015319847067849581 0
|
||||
688 0 -3.208489 0.008132398 0.011780537653460519 0
|
||||
689 0 -3.1226168 0.009109715 0.013202769109055056 0
|
||||
690 0 -3.571877 0.005025731 0.0072688781465224438 0
|
||||
691 1 3.3790636 0.9816168 0.026768161751618318 1
|
||||
692 0 -3.466956 0.0057758256 0.0083569122637117844 0
|
||||
693 0 -3.2114596 0.008100517 0.011734166675186883 0
|
||||
694 0 -3.0896535 0.009515041 0.013793026988723825 0
|
||||
695 0 -3.7254226 0.0040993094 0.0059262084034719161 0
|
||||
696 1 4.598935 0.9963301 0.0053043110546258845 1
|
||||
697 1 2.802182 0.9611624 0.05714790005987188 1
|
||||
698 1 3.3927562 0.98194325 0.026288446863364429 1
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Training 20 learners for the batch 1
|
||||
Beginning training model 1 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter)
|
||||
Trainer 1 of 20 finished in %Time%
|
||||
Beginning training model 2 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 2 of 20 finished in %Time%
|
||||
Beginning training model 3 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)
|
||||
Trainer 3 of 20 finished in %Time%
|
||||
Beginning training model 4 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 4 of 20 finished in %Time%
|
||||
Beginning training model 5 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 5 of 20 finished in %Time%
|
||||
Beginning training model 6 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 6 of 20 finished in %Time%
|
||||
Beginning training model 7 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)
|
||||
Trainer 7 of 20 finished in %Time%
|
||||
Beginning training model 8 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 8 of 20 finished in %Time%
|
||||
Beginning training model 9 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 9 of 20 finished in %Time%
|
||||
Beginning training model 10 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 10 of 20 finished in %Time%
|
||||
Beginning training model 11 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)
|
||||
Trainer 11 of 20 finished in %Time%
|
||||
Beginning training model 12 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)
|
||||
Trainer 12 of 20 finished in %Time%
|
||||
Beginning training model 13 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)
|
||||
Trainer 13 of 20 finished in %Time%
|
||||
Beginning training model 14 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)
|
||||
Trainer 14 of 20 finished in %Time%
|
||||
Beginning training model 15 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)
|
||||
Trainer 15 of 20 finished in %Time%
|
||||
Beginning training model 16 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 16 of 20 finished in %Time%
|
||||
Beginning training model 17 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 17 of 20 finished in %Time%
|
||||
Beginning training model 18 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)
|
||||
Trainer 18 of 20 finished in %Time%
|
||||
Beginning training model 19 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)
|
||||
Trainer 19 of 20 finished in %Time%
|
||||
Beginning training model 20 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 20 of 20 finished in %Time%
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 231 | 8 | 0.9665
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9585 | 0.9819 |
|
||||
OVERALL 0/1 ACCURACY: 0.973646
|
||||
LOG LOSS/instance: 0.116559
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.875205
|
||||
AUC: 0.995920
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.995920 (0.0000)
|
||||
Accuracy: 0.973646 (0.0000)
|
||||
Positive precision: 0.958506 (0.0000)
|
||||
Positive recall: 0.966527 (0.0000)
|
||||
Negative precision: 0.981900 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.116559 (0.0000)
|
||||
Log-loss reduction: 0.875205 (0.0000)
|
||||
F1 Score: 0.962500 (0.0000)
|
||||
AUPRC: 0.991714 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
WeightedEnsemble
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.99592 0.973646 0.958506 0.966527 0.9819 0.977477 0.116559 0.875205 0.9625 0.991714 Average Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Average;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -2.6282382 0.01783696 0.0259655617646397 0
|
||||
1 0 1.967058 0.9275491 3.7868530516332033 1
|
||||
2 0 -2.8538728 0.012990052 0.018863469141483385 0
|
||||
3 0 1.9359186 0.9245053 3.7274806974608468 1
|
||||
4 0 -2.598872 0.018586198 0.027066533968906802 0
|
||||
5 1 6.9540396 0.99993664 9.1411674874021938E-05 1
|
||||
6 0 -1.2390392 0.116509676 0.17871376040956136 0
|
||||
7 0 -3.165355 0.008367277 0.012122214657951928 0
|
||||
8 0 -3.2661564 0.007254287 0.010503869348196729 0
|
||||
9 0 -2.8366618 0.013308771 0.019329410426821694 0
|
||||
10 0 -3.6864796 0.0039948714 0.005774923887496365 0
|
||||
11 0 -3.5119026 0.0051193484 0.0074046282133417062 0
|
||||
12 1 -0.526532 0.26716384 1.904203323507824 0
|
||||
13 0 -3.079507 0.009447548 0.013694723201665347 0
|
||||
14 1 4.862514 0.9987481 0.00180720724421216 1
|
||||
15 1 0.54248196 0.62634546 0.6749695117196477 1
|
||||
16 0 -3.0233438 0.0102279065 0.014831728740476429 0
|
||||
17 0 -2.8725173 0.012653282 0.018371302375292598 0
|
||||
18 1 4.3502216 0.99740285 0.0037517741006701621 1
|
||||
19 0 -2.3839586 0.02509029 0.036659483516313734 0
|
||||
20 1 3.2561603 0.98774344 0.017791739126429732 1
|
||||
21 1 4.0615044 0.99608374 0.0056610664098227671 1
|
||||
22 0 -3.2676227 0.0072392323 0.010481991275315272 0
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
585 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
586 1 7.6839685 0.99997765 3.2247107427628552E-05 1
|
||||
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|
||||
588 1 3.1214504 0.9851839 0.021535051420384654 1
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
611 1 3.8206592 0.9944863 0.0079766383289292028 1
|
||||
612 1 8.576258 0.99999374 9.0291174246371028E-06 1
|
||||
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||||
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||||
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||||
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|
||||
617 0 ? ? ? 0
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
657 0 -0.15426195 0.38277665 0.69613544863505306 0
|
||||
658 1 4.735967 0.9985007 0.0021646489296086387 1
|
||||
659 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
660 0 -3.7561812 0.0036179798 0.0052291066583526171 0
|
||||
661 0 -2.8725173 0.012653282 0.018371302375292598 0
|
||||
662 0 -3.36474 0.006308216 0.0091296584365538197 0
|
||||
663 0 -3.36474 0.006308216 0.0091296584365538197 0
|
||||
664 0 -2.9939778 0.010660993 0.015463135069075847 0
|
||||
665 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
666 0 -2.3115225 0.027747018 0.040596340716738379 0
|
||||
667 0 -3.1167967 0.008962305 0.012988162625596602 0
|
||||
668 1 1.3648478 0.84425044 0.24425706774053077 1
|
||||
669 1 4.8292627 0.9986873 0.0018950310103747121 1
|
||||
670 1 3.769151 0.99406815 0.0085833395097514083 1
|
||||
671 0 -2.7932923 0.014146513 0.020554839486397272 0
|
||||
672 0 -3.3610759 0.006341079 0.0091773709814146308 0
|
||||
673 0 -2.2796817 0.028999556 0.042456139341540605 0
|
||||
674 0 -3.7561812 0.0036179798 0.0052291066583526171 0
|
||||
675 0 -2.587008 0.01889757 0.027524329928392318 0
|
||||
676 0 -3.5604606 0.0047782343 0.0069100570261159339 0
|
||||
677 0 -2.9298906 0.011670127 0.016935446480853662 0
|
||||
678 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
679 0 -3.662728 0.004132036 0.0059736181385521203 0
|
||||
680 1 8.686262 0.99999464 7.739240047104371E-06 1
|
||||
681 1 5.717595 0.9996302 0.00053358884622493365 1
|
||||
682 0 -2.4993777 0.021361273 0.031151719882885828 0
|
||||
683 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
684 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
685 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
686 0 -3.907008 0.0029193652 0.004217913491687578 0
|
||||
687 0 -3.0099025 0.010423933 0.015117486107810333 0
|
||||
688 0 -3.1741703 0.008263535 0.011971291770850049 0
|
||||
689 0 -3.213005 0.007821493 0.011328389385335143 0
|
||||
690 0 -3.633363 0.0043081194 0.0062287292265427417 0
|
||||
691 1 3.1120286 0.98498636 0.021824341396277779 1
|
||||
692 0 -3.4184494 0.0058454736 0.0084579803990395778 0
|
||||
693 0 -3.16848 0.008330354 0.012068497242296666 0
|
||||
694 0 -3.074401 0.009515989 0.013794407928597496 0
|
||||
695 0 -3.662728 0.004132036 0.0059736181385521203 0
|
||||
696 1 4.031519 0.99591327 0.0059079896641200755 1
|
||||
697 1 2.3933108 0.9592212 0.060064574083243262 1
|
||||
698 1 3.0079021 0.9826228 0.025290376944191735 1
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Training 3 learners for the batch 1
|
||||
Beginning training model 1 of 3
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 260 instances with missing features during training (over 10 iterations; 26 inst/iter)
|
||||
Trainer 1 of 3 finished in %Time%
|
||||
Beginning training model 2 of 3
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter)
|
||||
Trainer 2 of 3 finished in %Time%
|
||||
Beginning training model 3 of 3
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 110 instances with missing features during training (over 10 iterations; 11 inst/iter)
|
||||
Trainer 3 of 3 finished in %Time%
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 231 | 8 | 0.9665
|
||||
negative || 10 | 434 | 0.9775
|
||||
||======================
|
||||
Precision || 0.9585 | 0.9819 |
|
||||
OVERALL 0/1 ACCURACY: 0.973646
|
||||
LOG LOSS/instance: 0.112168
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.879907
|
||||
AUC: 0.996240
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996240 (0.0000)
|
||||
Accuracy: 0.973646 (0.0000)
|
||||
Positive precision: 0.958506 (0.0000)
|
||||
Positive recall: 0.966527 (0.0000)
|
||||
Negative precision: 0.981900 (0.0000)
|
||||
Negative recall: 0.977477 (0.0000)
|
||||
Log-loss: 0.112168 (0.0000)
|
||||
Log-loss reduction: 0.879907 (0.0000)
|
||||
F1 Score: 0.962500 (0.0000)
|
||||
AUPRC: 0.992400 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
WeightedEnsemble
|
||||
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
|
||||
0.99624 0.973646 0.958506 0.966527 0.9819 0.977477 0.112168 0.879907 0.9625 0.9924 AvgPer 3 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:AvgPer;/nm:3
|
||||
|
||||
|
|
@ -0,0 +1,700 @@
|
|||
Instance Label Score Probability Log-loss Assigned
|
||||
0 0 -3.4828715 0.022534288 0.032881999799805425 0
|
||||
1 0 2.9472694 0.89578646 3.2623854205088092 1
|
||||
2 0 -4.2678914 0.011065352 0.016052908381687058 0
|
||||
3 0 2.986453 0.89910704 3.3091025690874707 1
|
||||
4 0 -3.5019512 0.022150522 0.032315689216100618 0
|
||||
5 1 10.2867985 0.999865 0.00019478350072454816 1
|
||||
6 0 -2.3605843 0.06085541 0.090580805182194468 0
|
||||
7 0 -4.9923854 0.005709156 0.0082601727532068629 0
|
||||
8 0 -4.683963 0.007570115 0.010962915067788384 0
|
||||
9 0 -4.030381 0.013733364 0.0199503642579353 0
|
||||
10 0 -5.6011734 0.0032671653 0.0047212398797933366 0
|
||||
11 0 -5.476469 0.0036632805 0.0052947005483042842 0
|
||||
12 1 -0.42497015 0.27806875 1.84648646876029 0
|
||||
13 0 -5.052911 0.0054013343 0.0078135991288357728 0
|
||||
14 1 8.086931 0.9989771 0.0014764523778664522 1
|
||||
15 1 1.638258 0.7202816 0.47336704267911461 1
|
||||
16 0 -4.3381057 0.010379733 0.015053047889961934 0
|
||||
17 0 -4.0520535 0.01346566 0.019558824244149512 0
|
||||
18 1 7.524542 0.99828434 0.0024773000217939069 1
|
||||
19 0 -2.91369 0.037478272 0.055108986183136477 0
|
||||
20 1 6.2183647 0.9943106 0.00823148228811609 1
|
||||
21 1 7.010025 0.9972474 0.0039766409692087298 1
|
||||
22 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
23 1 ? ? ? 0
|
||||
24 0 -5.7595987 0.0028249216 0.0040812677703320503 0
|
||||
25 1 1.2282019 0.6383614 0.64755468922941795 1
|
||||
26 0 -4.4920645 0.009020099 0.013072298446733502 0
|
||||
27 0 -3.7689242 0.017406765 0.025333789038408744 0
|
||||
28 0 -5.476469 0.0036632805 0.0052947005483042842 0
|
||||
29 0 -5.9523435 0.002366593 0.0034183186220779643 0
|
||||
30 0 -4.7488623 0.0071340916 0.010329207257253776 0
|
||||
31 0 -5.190417 0.004761994 0.0068865147779546927 0
|
||||
32 1 6.630597 0.99610066 0.0056365490645245509 1
|
||||
33 0 -4.8385086 0.0065725353 0.0095134621707070616 0
|
||||
34 0 -4.7176075 0.0073408713 0.010629702115580997 0
|
||||
35 0 -5.476469 0.0036632805 0.0052947005483042842 0
|
||||
36 1 8.456722 0.9992721 0.0010505084408715286 1
|
||||
37 0 -1.3033304 0.14642774 0.22841479640447576 0
|
||||
38 1 4.9043865 0.9811732 0.027420241139352593 1
|
||||
39 1 0.9984417 0.5882377 0.7655288387710153 1
|
||||
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548 0 -5.034915 0.0054910923 0.0079438017710837285 0
|
||||
549 1 5.6715975 0.99062186 0.013593630704674639 1
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
565 1 9.47172 0.999714 0.00041264538703320383 1
|
||||
566 0 -4.4315553 0.009532037 0.013817783724351083 0
|
||||
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|
||||
568 1 4.041501 0.95925725 0.060010338652604994 1
|
||||
569 1 9.748816 0.9997784 0.00031975117930321329 1
|
||||
570 1 6.448406 0.99539167 0.0066637856180221805 1
|
||||
571 1 9.226857 0.9996417 0.00051698647656674178 1
|
||||
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|
||||
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|
||||
574 1 5.8652763 0.9921417 0.011381876111811766 1
|
||||
575 0 -3.293192 0.026720013 0.03907320553061365 0
|
||||
576 0 -3.7689242 0.017406765 0.025333789038408744 0
|
||||
577 0 -6.0456505 0.0021721658 0.0031371812770679881 0
|
||||
578 0 -6.0456505 0.0021721658 0.0031371812770679881 0
|
||||
579 0 -5.7595987 0.0028249216 0.0040812677703320503 0
|
||||
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|
||||
581 1 7.14419 0.9975665 0.0035150464757207813 1
|
||||
582 1 7.248555 0.99778897 0.0031933798554606417 1
|
||||
583 0 -5.19334 0.004749254 0.0068680469780405835 0
|
||||
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|
||||
585 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
586 1 11.944014 0.9999706 4.2394346321571357E-05 1
|
||||
587 0 -3.5992842 0.020290097 0.02957347196594819 0
|
||||
588 1 5.449993 0.98852324 0.016653203948794274 1
|
||||
589 0 -4.0549765 0.01342995 0.019506603784357205 0
|
||||
590 1 3.1383781 0.9111082 0.13430570798500757 1
|
||||
591 1 4.168865 0.963602 0.0534906972933578 1
|
||||
592 1 5.2628803 0.9863946 0.019763216273594907 1
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
601 0 -5.8872256 0.0025124764 0.0036292984276296014 0
|
||||
602 0 -4.3381057 0.010379733 0.015053047889961934 0
|
||||
603 1 3.4922361 0.9342013 0.098194641924081588 1
|
||||
604 1 4.188937 0.9642447 0.052528748937019649 1
|
||||
605 1 9.458144 0.99971044 0.0004178063517362282 1
|
||||
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|
||||
607 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
608 1 8.117621 0.9990056 0.0014353070238773355 1
|
||||
609 0 -4.0549765 0.01342995 0.019506603784357205 0
|
||||
610 1 8.266863 0.99913317 0.0012511141759918355 1
|
||||
611 1 6.1478367 0.9939311 0.0087822269655119054 1
|
||||
612 1 14.358189 0.99999684 4.5575474466196295E-06 1
|
||||
613 0 -4.861019 0.0064385654 0.0093189189633460356 0
|
||||
614 0 -5.3180437 0.0042362125 0.0061245443306950425 0
|
||||
615 0 -3.94002 0.014907228 0.021668496563689403 0
|
||||
616 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
617 0 ? ? ? 0
|
||||
618 0 -4.3381057 0.010379733 0.015053047889961934 0
|
||||
619 0 -3.7689242 0.017406765 0.025333789038408744 0
|
||||
620 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
621 0 -0.16198826 0.32918024 0.57600290997485726 0
|
||||
622 0 -1.7370644 0.103187285 0.15712136267093918 0
|
||||
623 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
624 0 -3.4554825 0.023096563 0.033712130719424638 0
|
||||
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|
||||
626 1 4.185855 0.96414673 0.052675368274755011 1
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||||
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|
||||
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||||
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||||
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||||
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||||
632 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
633 1 3.9723816 0.9566957 0.06386801737041177 1
|
||||
634 0 -5.19334 0.004749254 0.0068680469780405835 0
|
||||
635 0 -4.5400214 0.008633794 0.01251001466156154 0
|
||||
636 1 9.007486 0.99956155 0.0006326909022333528 1
|
||||
637 0 -2.404181 0.058601033 0.087121824844992937 0
|
||||
638 0 -4.6241584 0.007995274 0.011581101010143969 0
|
||||
639 0 -3.3673954 0.02499933 0.036524884369080135 0
|
||||
640 0 -3.9203415 0.015175687 0.022061716090628486 0
|
||||
641 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
642 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
643 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
644 0 -5.7625217 0.0028173495 0.0040703126069650368 0
|
||||
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|
||||
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|
||||
647 0 -5.627887 0.0031880299 0.0046067019330657986 0
|
||||
648 1 9.190195 0.99962944 0.00053470714742229219 1
|
||||
649 0 -4.9072876 0.0061716614 0.0089314146085223197 0
|
||||
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||||
651 0 -5.1132717 0.005110794 0.0073922236643752109 0
|
||||
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|
||||
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||||
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|
||||
655 0 -4.9072876 0.0061716614 0.0089314146085223197 0
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||||
656 0 -3.7689242 0.017406765 0.025333789038408744 0
|
||||
657 0 -0.46601295 0.2705457 0.45511048083845856 0
|
||||
658 1 8.120775 0.9990085 0.0014311753376832121 1
|
||||
659 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
660 0 -6.0456505 0.0021721658 0.0031371812770679881 0
|
||||
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|
||||
662 0 -5.5923867 0.0032936204 0.004759532206015593 0
|
||||
663 0 -5.5923867 0.0032936204 0.004759532206015593 0
|
||||
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|
||||
665 0 -6.331703 0.0016699884 0.002411297942606464 0
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||||
666 0 -2.9374223 0.036697846 0.053939703007611077 0
|
||||
667 0 -4.621235 0.008016653 0.011612193952652979 0
|
||||
668 1 2.1150284 0.79977554 0.32233293435036203 1
|
||||
669 1 7.066202 0.9973858 0.0037764318699177366 1
|
||||
670 1 5.4009867 0.98799986 0.017417262659521413 1
|
||||
671 0 -4.0500174 0.01349059 0.019595282853318546 0
|
||||
672 0 -5.190417 0.004761994 0.0068865147779546927 0
|
||||
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|
||||
674 0 -6.0456505 0.0021721658 0.0031371812770679881 0
|
||||
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|
||||
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|
||||
677 0 -4.0549765 0.01342995 0.019506603784357205 0
|
||||
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|
||||
679 0 -5.7625217 0.0028173495 0.0040703126069650368 0
|
||||
680 1 14.850252 0.999998 2.9237080272005804E-06 1
|
||||
681 1 8.980978 0.9995507 0.00064834827339000788 1
|
||||
682 0 -3.3132315 0.02624428 0.038368198335972985 0
|
||||
683 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
684 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
685 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
686 0 -6.331703 0.0016699884 0.002411297942606464 0
|
||||
687 0 -4.5680065 0.008415992 0.012193091213284017 0
|
||||
688 0 -4.6241584 0.007995274 0.011581101010143969 0
|
||||
689 0 -4.454012 0.009338769 0.013536300947270671 0
|
||||
690 0 -5.627887 0.0031880299 0.0046067019330657986 0
|
||||
691 1 4.4992037 0.97288966 0.039651900791925578 1
|
||||
692 0 -5.19334 0.004749254 0.0068680469780405835 0
|
||||
693 0 -4.6390457 0.007887273 0.011424041506677902 0
|
||||
694 0 -4.998421 0.0056776917 0.0082145194870921444 0
|
||||
695 0 -5.7625217 0.0028173495 0.0040703126069650368 0
|
||||
696 1 6.3037586 0.9947386 0.0076106653397057331 1
|
||||
697 1 3.8583193 0.95212936 0.070770491793538823 1
|
||||
698 1 4.445225 0.97154737 0.041643762631074922 1
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1
|
||||
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
|
||||
Training 20 learners for the batch 1
|
||||
Beginning training model 1 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 1 of 20 finished in %Time%
|
||||
Beginning training model 2 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 2 of 20 finished in %Time%
|
||||
Beginning training model 3 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 3 of 20 finished in %Time%
|
||||
Beginning training model 4 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)
|
||||
Trainer 4 of 20 finished in %Time%
|
||||
Beginning training model 5 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 5 of 20 finished in %Time%
|
||||
Beginning training model 6 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 6 of 20 finished in %Time%
|
||||
Beginning training model 7 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 7 of 20 finished in %Time%
|
||||
Beginning training model 8 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)
|
||||
Trainer 8 of 20 finished in %Time%
|
||||
Beginning training model 9 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)
|
||||
Trainer 9 of 20 finished in %Time%
|
||||
Beginning training model 10 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)
|
||||
Trainer 10 of 20 finished in %Time%
|
||||
Beginning training model 11 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)
|
||||
Trainer 11 of 20 finished in %Time%
|
||||
Beginning training model 12 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)
|
||||
Trainer 12 of 20 finished in %Time%
|
||||
Beginning training model 13 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 13 of 20 finished in %Time%
|
||||
Beginning training model 14 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)
|
||||
Trainer 14 of 20 finished in %Time%
|
||||
Beginning training model 15 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)
|
||||
Trainer 15 of 20 finished in %Time%
|
||||
Beginning training model 16 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)
|
||||
Trainer 16 of 20 finished in %Time%
|
||||
Beginning training model 17 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)
|
||||
Trainer 17 of 20 finished in %Time%
|
||||
Beginning training model 18 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter)
|
||||
Trainer 18 of 20 finished in %Time%
|
||||
Beginning training model 19 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)
|
||||
Trainer 19 of 20 finished in %Time%
|
||||
Beginning training model 20 of 20
|
||||
Warning: Training data does not support shuffling, so ignoring request to shuffle
|
||||
Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)
|
||||
Trainer 20 of 20 finished in %Time%
|
||||
List of models and the metrics after sorted
|
||||
| AUC(Sorted) || Name of Model |
|
||||
| 0.9969167523124358 |LinearBinaryModelParameters
|
||||
| 0.9966598150051388 |LinearBinaryModelParameters
|
||||
| 0.9966598150051388 |LinearBinaryModelParameters
|
||||
| 0.9966598150051388 |LinearBinaryModelParameters
|
||||
| 0.9965313463514902 |LinearBinaryModelParameters
|
||||
| 0.9964028776978417 |LinearBinaryModelParameters
|
||||
| 0.9964028776978417 |LinearBinaryModelParameters
|
||||
| 0.9962744090441932 |LinearBinaryModelParameters
|
||||
| 0.9962744090441932 |LinearBinaryModelParameters
|
||||
| 0.9961459403905447 |LinearBinaryModelParameters
|
||||
| 0.9961459403905447 |LinearBinaryModelParameters
|
||||
| 0.9961459403905447 |LinearBinaryModelParameters
|
||||
| 0.9958890030832477 |LinearBinaryModelParameters
|
||||
| 0.9957605344295992 |LinearBinaryModelParameters
|
||||
| 0.9956320657759506 |LinearBinaryModelParameters
|
||||
| 0.9955035971223022 |LinearBinaryModelParameters
|
||||
| 0.9952466598150052 |LinearBinaryModelParameters
|
||||
| 0.9947327852004111 |LinearBinaryModelParameters
|
||||
| 0.994218910585817 |LinearBinaryModelParameters
|
||||
| 0.9939619732785201 |LinearBinaryModelParameters
|
||||
Warning: 10 of 20 trainings failed.
|
||||
Training calibrator.
|
||||
Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
|
||||
TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))
|
||||
Confusion table
|
||||
||======================
|
||||
PREDICTED || positive | negative | Recall
|
||||
TRUTH ||======================
|
||||
positive || 229 | 10 | 0.9582
|
||||
negative || 9 | 435 | 0.9797
|
||||
||======================
|
||||
Precision || 0.9622 | 0.9775 |
|
||||
OVERALL 0/1 ACCURACY: 0.972182
|
||||
LOG LOSS/instance: 0.117306
|
||||
Test-set entropy (prior Log-Loss/instance): 0.934003
|
||||
LOG-LOSS REDUCTION (RIG): 0.874405
|
||||
AUC: 0.996042
|
||||
|
||||
OVERALL RESULTS
|
||||
---------------------------------------
|
||||
AUC: 0.996042 (0.0000)
|
||||
Accuracy: 0.972182 (0.0000)
|
||||
Positive precision: 0.962185 (0.0000)
|
||||
Positive recall: 0.958159 (0.0000)
|
||||
Negative precision: 0.977528 (0.0000)
|
||||
Negative recall: 0.979730 (0.0000)
|
||||
Log-loss: 0.117306 (0.0000)
|
||||
Log-loss reduction: 0.874405 (0.0000)
|
||||
F1 Score: 0.960168 (0.0000)
|
||||
AUPRC: 0.991960 (0.0000)
|
||||
|
||||
---------------------------------------
|
||||
Physical memory usage(MB): %Number%
|
||||
Virtual memory usage(MB): %Number%
|
||||
%DateTime% Time elapsed(s): %Number%
|
||||
|
||||
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