Uses of Interface
smile.validation.RegressionMeasure

Packages that use RegressionMeasure

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

Uses of RegressionMeasure in smile.feature

Methods in smile.feature with parameters of type RegressionMeasure

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 RegressionMeasure in smile.regression

Methods in smile.regression with parameters of type RegressionMeasure

Modifier and Type	Method and Description
double[][]	RandomForest.test(double[][] x, double[] y, RegressionMeasure[] measures) Test the model on a validation dataset.
double[][]	GradientTreeBoost.test(double[][] x, double[] y, RegressionMeasure[] measures) Test the model on a validation dataset.

Uses of RegressionMeasure in smile.validation

Classes in smile.validation that implement RegressionMeasure

Modifier and Type	Class and Description
class	AbsoluteDeviation Absolute deviation error.
class	MSE Mean squared error.
class	RMSE Root mean squared error.
class	RSS Residual sum of squares.

Methods in smile.validation with parameters of type RegressionMeasure

Modifier and Type	Method and Description
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, 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, 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.
static <T> double	Validation.test(Regression<T> regression, T[] x, double[] y, RegressionMeasure measure) Tests a regression model on a validation set.
static <T> double[]	Validation.test(Regression<T> regression, T[] x, double[] y, RegressionMeasure[] measures) Tests a regression model on a validation set.