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.
`classificationMetrics()`	Computes statistics to assess the quality of classification 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.
`lmPredict()`	Predict response using output from `lassoFit()`, `ridgeFit()`, or `ridgeCFit()` and matrix of independent variables.
`logisticRegFit()`	Fit a logistic regression model with an optional L1 and/or L2 penalty.
`meanSquaredError()`	Returns the mean squared error between two input vectors, or sets of vectors.
`oneHot()`	Returns a matrix of one-hot (indicator) variables from a vector or dataframe column.
`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()`.
`plotClasses()`	Plots predicted classes in color coded scatter plot.
`plotLR()`	Plots parameter path and mse path over regularization path after `lassoFit()` or `ridgeFit()`.
`plotVariableImportance()`	Plots variable importance table after decision forest regression.
`ridgeFit()`	Fit a linear model with an L2 penalty.
`ridgeCFit()`	Fit a binary classification model with an L2 penalty.
`ridgeCPredict()`	Predict binary classifications from `ridgeCFit()` model.
`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.

Optimization with Nonlinear Ineqaulity Constraints

binaryClassMetrics