From a2b763fa4a9c3f9a3ee7e56e30ce4632137ad3de Mon Sep 17 00:00:00 2001 From: Brandon Rohrer Date: Mon, 14 Mar 2016 11:29:35 -0400 Subject: [PATCH] Update README.md --- examples/MRS_and_Machine_Learning/README.md | 64 +++++++++++++++++++-- 1 file changed, 59 insertions(+), 5 deletions(-) diff --git a/examples/MRS_and_Machine_Learning/README.md b/examples/MRS_and_Machine_Learning/README.md index 3389b5a..26577b9 100644 --- a/examples/MRS_and_Machine_Learning/README.md +++ b/examples/MRS_and_Machine_Learning/README.md @@ -1,29 +1,83 @@ ## MRS_and_Machine_Learning This collection of examples shows how to use R and MRS to create machine learning models and showcases how to take advantage of the -functionality of Mircosoft R Server. +functionality of Mircosoft R Server. In order to run scripts with +`MRS` in the title, it will be necessary to first [install MRS] +(https://www.microsoft.com/en-us/server-cloud/products/r-server/). ### Flight_Delays_Prediction_with_R +* **R_Flight_Delays_with_MRS_Comparison.R** +This sample shows how to predict flight delays longer than 15 minutes using R, machine learning +and historical on-time performance and weather data . +When paired with `MRS_Flight_Delays_with_R_Comparison.R`, it provides a step-by-step comparison +of the functionality of open source R (a.k.a. CRAN R) and Microsoft R Server. + ### Flight_Delays_Prediction_with_MRS + +* **MRS_Flight_Delays_with_R_comparison.R** +This sample shows how to predict flight delays longer than 15 minutes using R, machine learning +and historical on-time performance and weather data . +When paired with `MRS_Flight_Delays_with_MRS_Comparison.R`, it provides a step-by-step comparison +of the functionality of open source R (a.k.a. CRAN R) and Microsoft R Server. + +* **MRS_Flight_Delays.R** +This sample shows how to build the same model as `MRS_Flight_Delays_with_R_comparison.R`, but +uses MRS best practices and syntax, which can differ substantially from those of R. ### Bike_Rental_Estimation_with_MRS * **MRS_Bike_Rental_Estimation.R** - A demand prediction model for bike rentals based on a historical data set. + This sample creates a demand prediction model for bike rentals based on a historical data set. It uses Microsoft R Server. -### R_MRO_MRS_Comparison - -### Benchmarks + +### R_MRO_MRS_Comparison + +* **R_MRO_MRS_Comparison_Part_1_Functions.R** +* **R_MRO_MRS_Comparison_Part_2_Capacity.R** +* **R_MRO_MRS_Comparison_Part_3_Speed.R** + +These samples show where the commands, syntax, constructs and performance of +R, Microsoft R Open and Microsoft R Server are similar, and where they differ. + +### Comparisons * **MRO-MKL-benchmarks.R** +Microsoft R Open includes the Intel Math Kernel Library (MKL) +for fast, parallel linear algebra +computations. This script runs performance benchmarks using different +numbers of threads. It requires MRS to be installed. + * **rxGlm-benchmark.R** +This sample demonstrates how to fit a logistic regression using CRAN R, +and how the rxGlm() function is dramatically faster and more scalable +NOTE: The CRAN portion of this comparison requires about 7GB of RAM. +If your machine has less, this script will crash. ### Machine Learning +* **Gradient Boosting Machine.R** +This sample shows how to create, train and evaluate +a gradient boosting machine model in R. + +* **LASSO Model.R** +This sample shows how to create, train and evaluate +a LASSO model in R. + +* **Linear Regression and Azure Web Service.R** +This sample shows how to create, train and evaluate +a linear regression model in R. It also shows how to deploy +that model as a web service in Azure Machine Learning. ### Data_Exploration * **Using_ggplot2.R** +This sample is an extension of the `A_First_Look_at_R/Introduction_to_ggplot2.R` sample. +It gives a more extensive tour of ggplot2's functionality including 3D plotting. + * **Import_Data_from_URL.R** +This sample shows how to load a URL-identified data file into R. + * **Import_Data_from_URL_to_xdf** +This sample shows how to load a URL-identified data file into MRS as an xdf. +It requires that MRS be installed.