Uses of Class
jsat.classifiers.ClassificationDataSet

Packages that use ClassificationDataSet

Package	Description
jsat
jsat.classifiers
jsat.classifiers.bayesian
jsat.classifiers.bayesian.graphicalmodel
jsat.classifiers.boosting
jsat.classifiers.calibration
jsat.classifiers.knn
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.classifiers.svm.extended
jsat.classifiers.trees
jsat.datatransform
jsat.datatransform.featureselection
jsat.io
jsat.linear.distancemetrics
jsat.parameters
jsat.regression
jsat.text

Uses of ClassificationDataSet in jsat

Methods in jsat that return ClassificationDataSet

Modifier and Type	Method and Description
ClassificationDataSet	SimpleDataSet.asClassificationDataSet(int index) Converts this dataset into one meant for classification problems.

Uses of ClassificationDataSet in jsat.classifiers

Methods in jsat.classifiers that return ClassificationDataSet

Modifier and Type	Method and Description
static ClassificationDataSet	ClassificationDataSet.comineAllBut(List<ClassificationDataSet> list, int exception) A helper method meant to be used with DataSet.cvSet(int), this combines all classification data sets in a given list, but holding out the indicated list.
protected ClassificationDataSet	ClassificationDataSet.getSubset(List<Integer> indicies)
ClassificationDataSet	ClassificationDataSet.getTwiceShallowClone()
ClassificationDataSet	ClassificationDataSet.shallowClone()

Methods in jsat.classifiers that return types with arguments of type ClassificationDataSet

Modifier and Type	Method and Description
List<ClassificationDataSet>	ClassificationDataSet.stratSet(int folds, Random rnd)

Methods in jsat.classifiers with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	ClassificationModelEvaluation.evaluateTestSet(ClassificationDataSet testSet) Performs an evaluation of the classifier using the initial data set to train, and testing on the given data set.
void	Rocchio.trainC(ClassificationDataSet dataSet)
void	RegressorToClassifier.trainC(ClassificationDataSet dataSet)
void	PriorClassifier.trainC(ClassificationDataSet dataSet)
void	OneVSOne.trainC(ClassificationDataSet dataSet)
void	OneVSAll.trainC(ClassificationDataSet dataSet)
void	MultinomialLogisticRegression.trainC(ClassificationDataSet dataSet)
void	MajorityVote.trainC(ClassificationDataSet dataSet)
void	Classifier.trainC(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	BaseUpdateableClassifier.trainC(ClassificationDataSet dataSet)
void	WarmClassifier.trainC(ClassificationDataSet dataSet, Classifier warmSolution) Trains the classifier and constructs a model for classification using the given data set.
void	WarmClassifier.trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.
void	Rocchio.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	RegressorToClassifier.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	PriorClassifier.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	OneVSOne.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	OneVSAll.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	MultinomialLogisticRegression.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	MajorityVote.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Classifier.trainC(ClassificationDataSet dataSet, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.
void	BaseUpdateableClassifier.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
static void	BaseUpdateableClassifier.trainEpochs(ClassificationDataSet dataSet, UpdateableClassifier toTrain, int epochs) Performs training on an updateable classifier by going over the whole data set in random order one observation at a time, multiple times.

Method parameters in jsat.classifiers with type arguments of type ClassificationDataSet

Modifier and Type	Method and Description
static ClassificationDataSet	ClassificationDataSet.comineAllBut(List<ClassificationDataSet> list, int exception) A helper method meant to be used with DataSet.cvSet(int), this combines all classification data sets in a given list, but holding out the indicated list.
void	ClassificationModelEvaluation.evaluateCrossValidation(List<ClassificationDataSet> lcds) Performs an evaluation of the classifier using the training data set, where the folds of the training data set are provided by the user.
void	ClassificationModelEvaluation.evaluateCrossValidation(List<ClassificationDataSet> lcds, List<ClassificationDataSet> trainCombinations) Note: Most people should never need to call this method.
void	ClassificationModelEvaluation.evaluateCrossValidation(List<ClassificationDataSet> lcds, List<ClassificationDataSet> trainCombinations) Note: Most people should never need to call this method.

Constructors in jsat.classifiers with parameters of type ClassificationDataSet

Constructor and Description
ClassificationModelEvaluation(Classifier classifier, ClassificationDataSet dataSet) Constructs a new object that can perform evaluations on the model.
ClassificationModelEvaluation(Classifier classifier, ClassificationDataSet dataSet, ExecutorService threadpool) Constructs a new object that can perform evaluations on the model.

Uses of ClassificationDataSet in jsat.classifiers.bayesian

Methods in jsat.classifiers.bayesian with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	NaiveBayes.trainC(ClassificationDataSet dataSet)
void	MultinomialNaiveBayes.trainC(ClassificationDataSet dataSet)
void	ConditionalProbabilityTable.trainC(ClassificationDataSet dataSet)
void	BestClassDistribution.trainC(ClassificationDataSet dataSet)
void	NaiveBayes.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	MultinomialNaiveBayes.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ConditionalProbabilityTable.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	BestClassDistribution.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	AODE.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ConditionalProbabilityTable.trainC(ClassificationDataSet dataSet, Set<Integer> categoriesToUse) Creates a CPT using only a subset of the features specified by categoriesToUse.

Uses of ClassificationDataSet in jsat.classifiers.bayesian.graphicalmodel

Methods in jsat.classifiers.bayesian.graphicalmodel with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
double	K2NetworkLearner.f(int i, Set<Integer> pi, ClassificationDataSet D)
void	K2NetworkLearner.learnNetwork(ClassificationDataSet D) Learns the network structure from the given data set.
void	K2NetworkLearner.trainC(ClassificationDataSet dataSet)
void	DiscreteBayesNetwork.trainC(ClassificationDataSet dataSet)
void	DiscreteBayesNetwork.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.boosting

Methods in jsat.classifiers.boosting that return ClassificationDataSet

Modifier and Type	Method and Description
static ClassificationDataSet	Bagging.getSampledDataSet(ClassificationDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
static ClassificationDataSet	Bagging.getWeightSampledDataSet(ClassificationDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.

Methods in jsat.classifiers.boosting with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
static ClassificationDataSet	Bagging.getSampledDataSet(ClassificationDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
static ClassificationDataSet	Bagging.getWeightSampledDataSet(ClassificationDataSet dataSet, int[] sampledCounts) Creates a new data set from the given sample counts.
void	Wagging.trainC(ClassificationDataSet dataSet)
void	UpdatableStacking.trainC(ClassificationDataSet dataSet)
void	Stacking.trainC(ClassificationDataSet dataSet)
void	SAMME.trainC(ClassificationDataSet dataSet)
void	ModestAdaBoost.trainC(ClassificationDataSet dataSet)
void	LogitBoost.trainC(ClassificationDataSet dataSet)
void	EmphasisBoost.trainC(ClassificationDataSet dataSet)
void	Bagging.trainC(ClassificationDataSet dataSet)
void	ArcX4.trainC(ClassificationDataSet dataSet)
void	AdaBoostM1PL.trainC(ClassificationDataSet dataSet)
void	AdaBoostM1.trainC(ClassificationDataSet dataSet)
void	Wagging.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	UpdatableStacking.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Stacking.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	SAMME.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ModestAdaBoost.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LogitBoostPL.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LogitBoost.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	EmphasisBoost.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Bagging.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ArcX4.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	AdaBoostM1PL.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	AdaBoostM1.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.calibration

Methods in jsat.classifiers.calibration with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	BinaryCalibration.trainC(ClassificationDataSet dataSet)
void	BinaryCalibration.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.knn

Methods in jsat.classifiers.knn with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	NearestNeighbour.trainC(ClassificationDataSet dataSet)
void	LWL.trainC(ClassificationDataSet dataSet)
void	DANN.trainC(ClassificationDataSet dataSet)
void	NearestNeighbour.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LWL.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DANN.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.linear

Methods in jsat.classifiers.linear with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
static double	LinearTools.maxLambdaLogisticL1(ClassificationDataSet cds) If the linear model performs logistic regression regularized by λ ||w||1, this method computes the smallest value of lambda that produces a weight vector of all zeros. Note, that the value returned depends on the data set size.
void	StochasticMultinomialLogisticRegression.trainC(ClassificationDataSet dataSet)
void	SMIDAS.trainC(ClassificationDataSet dataSet)
void	SCD.trainC(ClassificationDataSet dataSet)
void	PassiveAggressive.trainC(ClassificationDataSet dataSet)
void	NewGLMNET.trainC(ClassificationDataSet dataSet)
void	LogisticRegressionDCD.trainC(ClassificationDataSet dataSet)
void	LinearL1SCD.trainC(ClassificationDataSet dataSet)
void	LinearBatch.trainC(ClassificationDataSet dataSet)
void	BBR.trainC(ClassificationDataSet dataSet)
void	NewGLMNET.trainC(ClassificationDataSet dataSet, Classifier warmSolution)
void	LinearBatch.trainC(ClassificationDataSet dataSet, Classifier warmSolution)
void	NewGLMNET.trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)
void	LinearBatch.trainC(ClassificationDataSet D, Classifier warmSolution, ExecutorService threadPool)
void	StochasticMultinomialLogisticRegression.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	SMIDAS.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	SCD.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	PassiveAggressive.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	NewGLMNET.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LogisticRegressionDCD.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LinearL1SCD.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LinearBatch.trainC(ClassificationDataSet D, ExecutorService threadPool)
void	BBR.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Constructors in jsat.classifiers.linear with parameters of type ClassificationDataSet

Constructor and Description
LossMCFunction(ClassificationDataSet D, LossMC loss)

Uses of ClassificationDataSet in jsat.classifiers.linear.kernelized

Methods in jsat.classifiers.linear.kernelized with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	KernelSGD.trainC(ClassificationDataSet dataSet)
void	CSKLRBatch.trainC(ClassificationDataSet dataSet)
void	KernelSGD.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	CSKLRBatch.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.neuralnetwork

Methods in jsat.classifiers.neuralnetwork with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	SOM.trainC(ClassificationDataSet dataSet)
void	RBFNet.trainC(ClassificationDataSet dataSet)
void	Perceptron.trainC(ClassificationDataSet dataSet)
void	LVQLLC.trainC(ClassificationDataSet dataSet)
void	LVQ.trainC(ClassificationDataSet dataSet)
void	DReDNetSimple.trainC(ClassificationDataSet dataSet)
void	BackPropagationNet.trainC(ClassificationDataSet dataSet)
void	SOM.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	RBFNet.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Perceptron.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LVQLLC.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LVQ.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DReDNetSimple.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	BackPropagationNet.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Perceptron.trainCOnline(ClassificationDataSet dataSet)

Uses of ClassificationDataSet in jsat.classifiers.svm

Methods in jsat.classifiers.svm with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	SVMnoBias.trainC(ClassificationDataSet dataSet)
void	SBP.trainC(ClassificationDataSet dataSet)
void	PlattSMO.trainC(ClassificationDataSet dataSet)
void	PegasosK.trainC(ClassificationDataSet dataSet)
void	Pegasos.trainC(ClassificationDataSet dataSet)
void	LSSVM.trainC(ClassificationDataSet dataSet)
void	DCSVM.trainC(ClassificationDataSet dataSet)
void	DCDs.trainC(ClassificationDataSet dataSet)
void	DCD.trainC(ClassificationDataSet dataSet)
void	PlattSMO.trainC(ClassificationDataSet dataSet, Classifier warmSolution)
void	LSSVM.trainC(ClassificationDataSet dataSet, Classifier warmSolution)
void	DCDs.trainC(ClassificationDataSet dataSet, Classifier warmSolution)
void	PlattSMO.trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)
void	LSSVM.trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)
void	DCDs.trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)
protected void	SVMnoBias.trainC(ClassificationDataSet dataSet, double[] warm_start)
protected void	SVMnoBias.trainC(ClassificationDataSet dataSet, double[] warm_start, ExecutorService ex)
void	SVMnoBias.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	SBP.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	PlattSMO.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	PegasosK.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	Pegasos.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	LSSVM.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DCSVM.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DCDs.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DCD.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.svm.extended

Methods in jsat.classifiers.svm.extended with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	CPM.trainC(ClassificationDataSet dataSet)
void	AMM.trainC(ClassificationDataSet dataSet)
void	CPM.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	AMM.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.classifiers.trees

Methods in jsat.classifiers.trees with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
static void	TreePruner.prune(TreeNodeVisitor root, TreePruner.PruningMethod method, ClassificationDataSet testSet) Performs pruning starting from the root node of a tree
void	RandomForest.trainC(ClassificationDataSet dataSet)
void	ID3.trainC(ClassificationDataSet dataSet)
void	ExtraTree.trainC(ClassificationDataSet dataSet)
void	ERTrees.trainC(ClassificationDataSet dataSet)
void	DecisionTree.trainC(ClassificationDataSet dataSet)
void	DecisionStump.trainC(ClassificationDataSet dataSet)
void	RandomForest.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ID3.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ExtraTree.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	ERTrees.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DecisionTree.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DecisionStump.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	DecisionTree.trainC(ClassificationDataSet dataSet, Set<Integer> options)
protected void	DecisionTree.trainC(ClassificationDataSet dataSet, Set<Integer> options, ExecutorService threadPool) Performs exactly the same as DecisionTree.trainC(jsat.classifiers.ClassificationDataSet, java.util.concurrent.ExecutorService), but the user can specify a subset of the features to be considered.

Uses of ClassificationDataSet in jsat.datatransform

Methods in jsat.datatransform with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	DataModelPipeline.trainC(ClassificationDataSet dataSet)
void	DataModelPipeline.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.datatransform.featureselection

Constructors in jsat.datatransform.featureselection with parameters of type ClassificationDataSet

Constructor and Description
BDS(int featureCount, ClassificationDataSet dataSet, Classifier evaluator, int folds) Performs BDS feature selection for a classification problem
LRS(int L, int R, ClassificationDataSet cds, Classifier evaluater, int folds) Performs LRS feature selection for a classification problem
MutualInfoFS(ClassificationDataSet dataSet, int featureCount) Creates a new Mutual Information feature selection object.
MutualInfoFS(ClassificationDataSet dataSet, int featureCount, MutualInfoFS.NumericalHandeling numericHandling) Creates a new Mutual Information feature selection object.
ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm) Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, ExecutorService threadPool) Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, VectorCollectionFactory<Vec> vcf) Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
ReliefF(ClassificationDataSet cds, int featureCount, int m, int n, DistanceMetric dm, VectorCollectionFactory<Vec> vcf, ExecutorService threadPool) Creates a new ReliefF object to measure the importance of the variables with respect to a classification task.
SBS(int minFeatures, int maxFeatures, ClassificationDataSet cds, Classifier evaluater, int folds, double maxDecrease) Performs SBS feature selection for a classification problem
SFS(int minFeatures, int maxFeatures, ClassificationDataSet dataSet, Classifier evaluater, int folds, double maxIncrease) Performs SFS feature selection for a classification problem

Uses of ClassificationDataSet in jsat.io

Methods in jsat.io that return ClassificationDataSet

Modifier and Type	Method and Description
static ClassificationDataSet	LIBSVMLoader.loadC(File file) Loads a new classification data set from a LIBSVM file, assuming the label is a nominal target value
static ClassificationDataSet	LIBSVMLoader.loadC(File file, double sparseRatio) Loads a new classification data set from a LIBSVM file, assuming the label is a nominal target value
static ClassificationDataSet	LIBSVMLoader.loadC(File file, double sparseRatio, int vectorLength) Loads a new classification data set from a LIBSVM file, assuming the label is a nominal target value
static ClassificationDataSet	LIBSVMLoader.loadC(InputStreamReader isr, double sparseRatio) Loads a new classification data set from a LIBSVM file, assuming the label is a nominal target value
static ClassificationDataSet	LIBSVMLoader.loadC(Reader reader, double sparseRatio, int vectorLength) Loads a new classification data set from a LIBSVM file, assuming the label is a nominal target value
static ClassificationDataSet	JSATData.loadClassification(InputStream inRaw) Loads in a JSAT dataset as a ClassificationDataSet.
static ClassificationDataSet	CSV.readC(int classification_target, Path path, char delimiter, int lines_to_skip, char comment, Set<Integer> cat_cols) Reads in a CSV dataset as a classification dataset.
static ClassificationDataSet	CSV.readC(int classification_target, Path path, int lines_to_skip, Set<Integer> cat_cols) Reads in a CSV dataset as a classification dataset.
static ClassificationDataSet	CSV.readC(int classification_target, Reader reader, char delimiter, int lines_to_skip, char comment, Set<Integer> cat_cols) Reads in a CSV dataset as a classification dataset.
static ClassificationDataSet	CSV.readC(int classification_target, Reader reader, int lines_to_skip, Set<Integer> cat_cols) Reads in a CSV dataset as a classification dataset.

Methods in jsat.io with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
static void	LIBSVMLoader.write(ClassificationDataSet data, OutputStream os) Writes out the given classification data set as a LIBSVM data file

Uses of ClassificationDataSet in jsat.linear.distancemetrics

Methods in jsat.linear.distancemetrics with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
abstract void	TrainableDistanceMetric.train(ClassificationDataSet dataSet) Trains this metric on the given classification problem data set
void	NormalizedEuclideanDistance.train(ClassificationDataSet dataSet)
void	MahalanobisDistance.train(ClassificationDataSet dataSet)
abstract void	TrainableDistanceMetric.train(ClassificationDataSet dataSet, ExecutorService threadpool) Trains this metric on the given classification problem data set
void	NormalizedEuclideanDistance.train(ClassificationDataSet dataSet, ExecutorService threadpool)
void	MahalanobisDistance.train(ClassificationDataSet dataSet, ExecutorService threadpool)

Uses of ClassificationDataSet in jsat.parameters

Methods in jsat.parameters with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	RandomSearch.trainC(ClassificationDataSet dataSet)
void	GridSearch.trainC(ClassificationDataSet dataSet)
void	RandomSearch.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
void	GridSearch.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.regression

Methods in jsat.regression with parameters of type ClassificationDataSet

Modifier and Type	Method and Description
void	LogisticRegression.trainC(ClassificationDataSet dataSet)
void	LogisticRegression.trainC(ClassificationDataSet dataSet, ExecutorService threadPool)

Uses of ClassificationDataSet in jsat.text

Methods in jsat.text that return ClassificationDataSet

Modifier and Type	Method and Description
ClassificationDataSet	ClassificationTextDataLoader.getDataSet()
ClassificationDataSet	ClassificationHashedTextDataLoader.getDataSet()