Uses of Interface
smile.validation.ClassificationMeasure

Packages that use ClassificationMeasure

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

Uses of ClassificationMeasure in smile.classification

Methods in smile.classification with parameters of type ClassificationMeasure

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

Uses of ClassificationMeasure in smile.feature

Methods in smile.feature with parameters of type ClassificationMeasure

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 ClassificationMeasure in smile.validation

Classes in smile.validation that implement ClassificationMeasure

Modifier and Type	Class and Description
class	Accuracy The accuracy is the proportion of true results (both true positives and true negatives) in the population.
class	Fallout Fall-out, false alarm rate, or false positive rate (FPR)
class	FDR The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
class	FMeasure The F-measure (also F1 score or F-score) considers both the precision p and the recall r of the test to compute the score.
class	Precision The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.
class	Recall In information retrieval area, sensitivity is called recall.
class	Sensitivity Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test.
class	Specificity Specificity (SPC) or True Negative Rate is a statistical measures of the performance of a binary classification test.

Methods in smile.validation with parameters of type ClassificationMeasure

Modifier and Type	Method and Description
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, 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, 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.
static <T> double	Validation.test(Classifier<T> classifier, T[] x, int[] y, ClassificationMeasure measure) Tests a classifier on a validation set.
static <T> double[]	Validation.test(Classifier<T> classifier, T[] x, int[] y, ClassificationMeasure[] measures) Tests a classifier on a validation set.