Uses of Class
smile.classification.ClassifierTrainer

Packages that use ClassifierTrainer

Package	Description
smile.classification	Classification algorithms.
smile.feature	Feature generation, normalization and selection.
smile.validation	Model validation.

Uses of ClassifierTrainer in smile.classification

Subclasses of ClassifierTrainer in smile.classification

Modifier and Type	Class and Description
static class	AdaBoost.Trainer Trainer for AdaBoost classifiers.
static class	DecisionTree.Trainer Trainer for decision tree classifiers.
static class	FLD.Trainer Trainer for Fisher's linear discriminant.
static class	GradientTreeBoost.Trainer Trainer for GradientTreeBoost classifiers.
static class	KNN.Trainer<T> Trainer for KNN classifier.
static class	LDA.Trainer Trainer for linear discriminant analysis.
static class	LogisticRegression.Trainer Trainer for logistic regression.
static class	Maxent.Trainer Trainer for maximum entropy classifier.
static class	NaiveBayes.Trainer Trainer for naive Bayes classifier for document classification.
static class	NeuralNetwork.Trainer Trainer for neural networks.
static class	QDA.Trainer Trainer for quadratic discriminant analysis.
static class	RandomForest.Trainer Trainer for random forest classifiers.
static class	RBFNetwork.Trainer<T> Trainer for RBF networks.
static class	RDA.Trainer Trainer for regularized discriminant analysis.
static class	SVM.Trainer<T> Trainer for support vector machines.

Uses of ClassifierTrainer in smile.feature

Methods in smile.feature with parameters of type ClassifierTrainer

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

Uses of ClassifierTrainer in smile.validation

Methods in smile.validation with parameters of type ClassifierTrainer

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