* fixed Python-API references
* moved Features section to ReadTheDocs
* fixed index of ReadTheDocs
* moved Experiments section to ReadTheDocs
* fixed capital letter
* fixed citing
* moved Parallel Learning section to ReadTheDocs
* fixed markdown
* fixed Python-API
* fixed link to Quick-Start
* fixed gpu docker README
* moved Installation Guide from wiki to ReadTheDocs
* removed references to wiki
* fixed capital letters in headings
* hotfixes
* fixed non-Unicode symbols and reference to Python API
* fixed citing references
* fixed links in .md files
* fixed links in .rst files
* store images locally in the repo
* fixed missed word
* fixed indent in Experiments.rst
* fixed 'Duplicate implicit target name' message which is successfully
resolved by adding anchors
* less verbose
* prevented maito: ref creation
* fixed indents
* fixed 404
* fixed 403
* fixed 301
* fixed fake anchors
* fixed file extentions
* fixed Sphinx warnings
* added StrikerRUS profile link to FAQ
* added henry0312 profile link to FAQ
* disabled logs from compilers; fixed#874
* fixed safe clear_fplder
* added windows folder to manifest.in
* added windows folder to build
* added library path
* added compilation with MSBuild from .sln-file
* fixed unknown PlatformToolset returns exitcode 0
* hotfix
* updated Readme
* removed return
* added installation with mingw test to appveyor
* let's test appveyor with both VS 2015 and VS 2017; but MinGW isn't installed on VS 2017 image
* fixed built-in name 'file'
* simplified appveyor
* removed excess data_files
* fixed unreadable paths
* separated exceptions for cmake and mingw
* refactored silent_call
* don't create artifacts with VS 2015 and mingw
* be more precise with python versioning in Travis
* removed unnecessary if statement
* added classifiers for PyPI and python versions badge
* changed python version in travis
* added support of scikit-learn 0.18.x
* added more python versions to Travis
* added more python versions to Appveyor
* reduced number of tests in Travis
* Travis trick is not needed anymore
* attempt to fix according to https://github.com/Microsoft/LightGBM/pull/880#discussion_r137438856
* add dummy gpu solver code
* initial GPU code
* fix crash bug
* first working version
* use asynchronous copy
* use a better kernel for root
* parallel read histogram
* sparse features now works, but no acceleration, compute on CPU
* compute sparse feature on CPU simultaneously
* fix big bug; add gpu selection; add kernel selection
* better debugging
* clean up
* add feature scatter
* Add sparse_threshold control
* fix a bug in feature scatter
* clean up debug
* temporarily add OpenCL kernels for k=64,256
* fix up CMakeList and definition USE_GPU
* add OpenCL kernels as string literals
* Add boost.compute as a submodule
* add boost dependency into CMakeList
* fix opencl pragma
* use pinned memory for histogram
* use pinned buffer for gradients and hessians
* better debugging message
* add double precision support on GPU
* fix boost version in CMakeList
* Add a README
* reconstruct GPU initialization code for ResetTrainingData
* move data to GPU in parallel
* fix a bug during feature copy
* update gpu kernels
* update gpu code
* initial port to LightGBM v2
* speedup GPU data loading process
* Add 4-bit bin support to GPU
* re-add sparse_threshold parameter
* remove kMaxNumWorkgroups and allows an unlimited number of features
* add feature mask support for skipping unused features
* enable kernel cache
* use GPU kernels withoug feature masks when all features are used
* REAdme.
* REAdme.
* update README
* fix typos (#349)
* change compile to gcc on Apple as default
* clean vscode related file
* refine api of constructing from sampling data.
* fix bug in the last commit.
* more efficient algorithm to sample k from n.
* fix bug in filter bin
* change to boost from average output.
* fix tests.
* only stop training when all classes are finshed in multi-class.
* limit the max tree output. change hessian in multi-class objective.
* robust tree model loading.
* fix test.
* convert the probabilities to raw score in boost_from_average of classification.
* fix the average label for binary classification.
* Add boost_from_average to docs (#354)
* don't use "ConvertToRawScore" for self-defined objective function.
* boost_from_average seems doesn't work well in binary classification. remove it.
* For a better jump link (#355)
* Update Python-API.md
* for a better jump in page
A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/)
After adding the spaces, we can jump to the exact position in page by click the link.
* fixed something mentioned by @wxchan
* Update Python-API.md
* add FitByExistingTree.
* adapt GPU tree learner for FitByExistingTree
* avoid NaN output.
* update boost.compute
* fix typos (#361)
* fix broken links (#359)
* update README
* disable GPU acceleration by default
* fix image url
* cleanup debug macro
* remove old README
* do not save sparse_threshold_ in FeatureGroup
* add details for new GPU settings
* ignore submodule when doing pep8 check
* allocate workspace for at least one thread during builing Feature4
* move sparse_threshold to class Dataset
* remove duplicated code in GPUTreeLearner::Split
* Remove duplicated code in FindBestThresholds and BeforeFindBestSplit
* do not rebuild ordered gradients and hessians for sparse features
* support feature groups in GPUTreeLearner
* Initial parallel learners with GPU support
* add option device, cleanup code
* clean up FindBestThresholds; add some omp parallel
* constant hessian optimization for GPU
* Fix GPUTreeLearner crash when there is zero feature
* use np.testing.assert_almost_equal() to compare lists of floats in tests
* travis for GPU
* add tutorial and more GPU docs
Tsukasa OMOTO
Nikita Titov
Guolin Ke
William Entriken
Boris Kostenko
Qiwei Ye
The Gitter Badger
Laurae
Huan Zhang
Preston Parry
wxchan
Allardvm
xuehui
guFalcon