Uses of Class
smile.regression.RegressionTrainer

Packages that use RegressionTrainer

Package	Description
smile.feature	Feature generation, normalization and selection.
smile.regression	Regression analysis.
smile.validation	Model validation.

Uses of RegressionTrainer in smile.feature

Methods in smile.feature with parameters of type RegressionTrainer

Modifier and Type	Method and Description
BitString[]	GAFeatureSelection.learn(int size, int generation, RegressionTrainer<double[]> trainer, RegressionMeasure measure, double[][] x, double[] y, double[][] testx, double[] testy) Genetic algorithm based feature selection for regression.
BitString[]	GAFeatureSelection.learn(int size, int generation, RegressionTrainer<double[]> trainer, RegressionMeasure measure, double[][] x, double[] y, int k) Genetic algorithm based feature selection for regression.

Uses of RegressionTrainer in smile.regression

Subclasses of RegressionTrainer in smile.regression

Modifier and Type	Class and Description
static class	GaussianProcessRegression.Trainer<T> Trainer for Gaussian Process for Regression.
static class	GradientTreeBoost.Trainer Trainer for GradientTreeBoost regression.
static class	LASSO.Trainer Trainer for LASSO regression.
static class	OLS.Trainer Trainer for linear regression by ordinary least squares.
static class	RandomForest.Trainer Trainer for random forest.
static class	RBFNetwork.Trainer<T> Trainer for RBF networks.
static class	RegressionTree.Trainer Trainer for regression tree.
static class	RidgeRegression.Trainer Trainer for ridge regression.
static class	SVR.Trainer<T> Trainer for support vector regression.

Uses of RegressionTrainer in smile.validation

Methods in smile.validation with parameters of type RegressionTrainer

Modifier and Type	Method and Description
static <T> double[]	Validation.bootstrap(int k, RegressionTrainer<T> trainer, T[] x, double[] y) Bootstrap RMSE estimation of a regression model.
static <T> double[]	Validation.bootstrap(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) Bootstrap performance estimation of a regression model.
static <T> double[][]	Validation.bootstrap(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure[] measures) Bootstrap performance estimation of a regression model.
static <T> double	Validation.cv(int k, RegressionTrainer<T> trainer, T[] x, double[] y) Cross validation of a regression model.
static <T> double	Validation.cv(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) Cross validation of a regression model.
static <T> double[]	Validation.cv(int k, RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure[] measures) Cross validation of a regression model.
static <T> double	Validation.loocv(RegressionTrainer<T> trainer, T[] x, double[] y) Leave-one-out cross validation of a regression model.
static <T> double	Validation.loocv(RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure measure) Leave-one-out cross validation of a regression model.
static <T> double[]	Validation.loocv(RegressionTrainer<T> trainer, T[] x, double[] y, RegressionMeasure[] measures) Leave-one-out cross validation of a regression model.