# GAUSS Machine Learning (GML)¶

## Description¶

Provides tools to model, analyze, and predict data using fundamental machine learning techniques.

## Installation¶

 binaryClassMetrics() Computes statistics to assess the quality of binary predictions and prints out a report. cvSplit() Returns the test and training set for the ith of k cross validation splits for a given set of dependent and independent variables. decForestCFit() Fit a decision forest classification model. decForestPredict() Predicts responses using the output from decForestCFit() or decForestRFit() and matrix of independent variables. decForestRFit() Fit a decision forest regression model. kmeansFit() Partitions data into k clusters, using the kmeans algorithm. kmeansPredict() Partitions data into k clusters, based upon k user supplied centroids. knnFit() Creates a K-D tree model from training data for efficient KNN predictions. knnClassify() Creates nearest neighbor predictions. lassoFit() Fit a linear model with an L1 penalty. pcaFit() Performs principal component dimension reduction. pcaTransform() Reduces the dimension of a matrix using principal component vectors previously returned by pcaFit(). pcaTransformInv() Transforms a matrix back to the original feature space of the X which was input to pcaFit(). ridgeFit() Fit a linear model with an L1 penalty. splitData() Returns test and training splits for a single matrix of variables. trainTestSplit() Returns test and training splits for a given set of dependent and independent variables.