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.

Item | Data |
---|---|

Package | MicrosoftML |

Type | Package |

Version | 1.0.0 |

License | file LICENSE |

LazyLoad | yes |

## 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.

### Helper functions

**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.**

- optimizer Specifies optimization algorithms for the rxNeuralNet machine learning algorithm.

## What's next?

Diving into data analysis in Microsoft R

Cheat Sheet: How to choose a MicrosoftML algorithm

## Microsoft Technical Support

To request technical support from the Microsoft Corporation, click on `Microsoft Technical Support`

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