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. Requires GAUSS 22 or higher. Commands ---------------------- =============================== ======================================================================================================================================== :func:`binaryClassMetrics` Computes statistics to assess the quality of binary predictions and prints out a report. :func:`classificationMetrics` Computes statistics to assess the quality of classification predictions and prints out a report. :func:`cvSplit` Returns the test and training set for the ith of k cross validation splits for a given set of dependent and independent variables. :func:`decForestCFit` Fit a decision forest classification model. :func:`decForestPredict` Predicts responses using the output from :func:`decForestCFit` or :func:`decForestRFit` and matrix of independent variables. :func:`decForestRFit` Fit a decision forest regression model. :func:`kmeansFit` Partitions data into k clusters, using the kmeans algorithm. :func:`kmeansPredict` Partitions data into k clusters, based upon k user supplied centroids. :func:`knnFit` Creates a K-D tree model from training data for efficient KNN predictions. :func:`knnClassify` Creates nearest neighbor predictions. :func:`lassoFit` Fit a linear model with an L1 penalty. :func:`lmPredict` Predict response using output from :func:`lassoFit`, :func:`ridgeFit`, or :func:`ridgeCFit` and matrix of independent variables. :func:`logisticRegFit` Fit a logistic regression model with an optional L1 and/or L2 penalty. :func:`meanSquaredError` Returns the mean squared error between two input vectors, or sets of vectors. :func:`oneHot` Returns a matrix of one-hot (indicator) variables from a vector or dataframe column. :func:`pcaFit` Performs principal component dimension reduction. :func:`pcaTransform` Reduces the dimension of a matrix using principal component vectors previously returned by :func:`pcaFit`. :func:`pcaTransformInv` Transforms a matrix back to the original feature space of the X which was input to :func:`pcaFit`. :func:`plotClasses` Plots predicted classes in color coded scatter plot. :func:`plotLR` Plots parameter path and mse path over regularization path after :func:`lassoFit` or :func:`ridgeFit`. :func:`plotVariableImportance` Plots variable importance table after decision forest regression. :func:`ridgeFit` Fit a linear model with an L2 penalty. :func:`ridgeCFit` Fit a binary classification model with an L2 penalty. :func:`ridgeCPredict` Predict binary classifications from :func:`ridgeCFit` model. :func:`splitData` Returns test and training splits for a single matrix of variables. :func:`trainTestSplit` Returns test and training splits for a given set of dependent and independent variables. =============================== ======================================================================================================================================== .. toctree:: :maxdepth: 1 :hidden: binaryclassquality classificationmetrics cvsplit decforestcfit decforestpredict decforestrfit kmeansfit kmeanspredict knnfit knnclassify lassofit lmpredict logisticregfit meansquarederror onehot pcafit pcatransform pcatransforminv plotclasses plotlr plotvariableimportance ridgecfit ridgecpredict ridgefit splitdata traintestsplit