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¶
Computes statistics to assess the quality of binary predictions and prints out a report. |
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Returns the test and training set for the ith of k cross validation splits for a given set of dependent and independent variables. |
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Fit a decision forest classification model. |
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Predicts responses using the output from |
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Fit a decision forest regression model. |
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Partitions data into k clusters, using the kmeans algorithm. |
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Partitions data into k clusters, based upon k user supplied centroids. |
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Creates a K-D tree model from training data for efficient KNN predictions. |
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Creates nearest neighbor predictions. |
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Fit a linear model with an L1 penalty. |
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Predict response using output from |
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Performs principal component dimension reduction. |
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Reduces the dimension of a matrix using principal component vectors previously returned by |
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Transforms a matrix back to the original feature space of the X which was input to |
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Plots predicted classes in color coded scatter plot. |
Plots parameter path and mse path over regularization path after |
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Fit a linear model with an L2 penalty. |
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Fit a binary classification model with an L2 penalty. |
Predict binary classifications from |
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Returns test and training splits for a single matrix of variables. |
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Returns test and training splits for a given set of dependent and independent variables. |