The MicrosoftML package provides state-of-the-art fast, scalable machine learning algorithms and transforms for R. The package is used with the RevoScaleR package.
This topic includes links to the reference documentation for the ML algorithms and transforms, and for the scoring and helper functions.
Key algorithms and transforms in the package
Machine learning algorithms
- 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.
Machine learning transforms
- 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.
- 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.
Scoring and training and model summary
- 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.
Loss functions for classification and regression.
- 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.
Functions for feature selection.
- minCount: Specification for feature selection in count mode.
- mutualInformation: Specification for feature selection in mutual information mode.
Functions for ensemble modeling.
- 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.
- oneClassSvmCreates a list containing the function name and arguments to train a OneClassSvm model with rxEnsemble.
Functions for neural networks.
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