GAUSS Machine Learning (GML)

Description

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

Installation

Please contact us directly to request a copy of the GAUSS Machine Learning library.

Commands

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.