MicrosoftML package

The MicrosoftML library provides state-of-the-art fast, scalable machine learning algorithms and transforms for R. The package is used with the RevoScaleR package.

Package details
Current version: 9.4.0
Built on: R 3.5.2
Package distribution: Machine Learning Server 9.x
Microsoft R Client (Windows and Linux)
Microsoft R Server 9.1
SQL Server 2016 and later (Windows only)
Azure HDInsight
Azure Data Science Virtual Machines

How to use MicrosoftML for R

The MicrosoftML module is installed as part of Microsoft Machine Learning Server or SQL Server Machine Learning Server when you add R to your installation. It is also installed with the pre-trained machine learning models. You can use any R IDE to write R script calling functions in MicrosoftML, but the script must run on a computer having our interpreters and libraries.

Use this library with RevoScaleR data sources.

Functions by category

This section lists the functions by category to give you an idea of how each one is used. You can also use the table of contents to find functions in alphabetical order.

1-Machine learning algorithms

Function name Description
rxFastTrees An implementation of FastRank, an efficient implementation of the MART gradient boosting algorithm.
rxFastForest A random forest and Quantile regression forest implementation using rxFastTrees.
rxLogisticRegression Logistic regression using L-BFGS.
rxOneClassSvm One class support vector machines.
rxNeuralNet Binary, multi-class, and regression neural net.
rxFastLinear Stochastic dual coordinate ascent optimization for linear binary classification and regression.
rxEnsemble Trains a number of models of various kinds to obtain better predictive performance than could be obtained from a single model.

2-Transformation functions

Function name Description
concat Transformation to create a single vector-valued column from multiple columns.
categorical Create indicator vector using categorical transform with dictionary.
categoricalHash Converts the categorical value into an indicator array by hashing.
featurizeText Produces a bag of counts of sequences of consecutive words, called n-grams, from a given corpus of text. It offers language detection, tokenization, stopwords removing, text normalization and feature generation.
getSentiment Scores natural language text and creates a column that contains probabilities that the sentiments in the text are positive.
ngram allows defining arguments for count-based and hash-based feature extraction.
selectColumns Selects a set of columns to retrain, dropping all others.
selectFeatures Selects features from the specified variables using a specified mode.
loadImage Loads image data.
resizeImage Resizes an image to a specified dimension using a specified resizing method.
extractPixels Extracts the pixel values from an image.
featurizeImage Featurizes an image using a pre-trained deep neural network model.

3-Scoring and training functions

Function name Description
rxPredict.mlModel Runs the scoring library either from SQL Server, using the stored procedure, or from R code enabling real-time scoring to provide much faster prediction performance.
rxFeaturize Transforms data from an input data set to an output data set.
mlModel Provides a summary of a Microsoft R Machine Learning model.

4-Loss functions for classification and regression

Function name Description
expLoss Specifications for exponential classification loss function.
logLoss Specifications for log classification loss function.
hingeLoss Specifications for hinge classification loss function.
smoothHingeLoss Specifications for smooth hinge classification loss function.
poissonLoss Specifications for poisson regression loss function.
squaredLoss Specifications for squared regression loss function.

5-Feature selection functions

Function name Description
minCount Specification for feature selection in count mode.
mutualInformation Specification for feature selection in mutual information mode.

6-Ensemble modeling functions

Function name Description
fastTrees Creates a list containing the function name and arguments to train a Fast Tree model with rxEnsemble.
fastForest Creates a list containing the function name and arguments to train a Fast Forest model with rxEnsemble.
fastLinear Creates a list containing the function name and arguments to train a Fast Linear model with rxEnsemble.
logisticRegression Creates a list containing the function name and arguments to train a Logistic Regression model with rxEnsemble.
oneClassSvm Creates a list containing the function name and arguments to train a OneClassSvm model with rxEnsemble.

7-Neural networking functions

Function name Description
optimizer Specifies optimization algorithms for the rxNeuralNet machine learning algorithm.

8-Package state functions

Function name Description
rxHashEnv An environment object used to store package-wide state.

Next steps

Add R packages to your computer by running setup for Machine Learning Server or R Client:

Next, refer to these introductory articles and samples to get started:

See also

R Package Reference