Uses of Interface
jsat.classifiers.Classifier

Packages that use Classifier

Package	Description
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.parameters
jsat.regression

Uses of Classifier in jsat.classifiers

Subinterfaces of Classifier in jsat.classifiers

Modifier and Type	Interface and Description
interface	UpdateableClassifier UpdateableClassifier is an interface for one type of Online learner.
interface	WarmClassifier This interface is meant for models that support efficient warm starting from the solution of a previous model.

Classes in jsat.classifiers that implement Classifier

Modifier and Type	Class and Description
class	BaseUpdateableClassifier A base implementation of the UpdateableClassifier.
class	DDAG Decision Directed Acyclic Graph (DDAG) classifier.
class	MajorityVote The Majority Vote classifier is a simple ensemble classifier.
class	MultinomialLogisticRegression Multinomial Logistic Regression is an extension of LogisticRegression for classification when there are more then two target classes.
class	OneVSAll This classifier turns any classifier, specifically binary classifiers, into multi-class classifiers.
class	OneVSOne A One VS One classifier extends binary decision classifiers into multi-class decision classifiers.
class	PriorClassifier A Naive classifier that simply returns the prior probabilities as the classification decision.
class	RegressorToClassifier This meta algorithm wraps a Regressor to perform binary classification.
class	Rocchio

Fields in jsat.classifiers declared as Classifier

Modifier and Type	Field and Description
protected Classifier	OneVSOne.baseClassifier Main binary classifier
protected Classifier[][]	OneVSOne.oneVone Uper-diagonal matrix of classifiers sans the first index since a classifier vs itself is useless.

Methods in jsat.classifiers that return Classifier

Modifier and Type	Method and Description
Classifier	PriorClassifier.clone()
Classifier	MajorityVote.clone()
Classifier	Classifier.clone()
Classifier	ClassificationModelEvaluation.getClassifier() Returns the classifier that was original given for evaluation.
Classifier[]	ClassificationModelEvaluation.getKeptModels() Returns the models that were kept after the last evaluation.

Methods in jsat.classifiers with parameters of type Classifier

Modifier and Type	Method and Description
void	ClassificationModelEvaluation.setWarmModels(Classifier... warmModels) Sets the models that will be used for warm starting training.
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.

Constructors in jsat.classifiers with parameters of type Classifier

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.
DDAG(Classifier baseClassifier) Creates a new DDAG classifier to extend a binary classifier to handle multi-class problems.
DDAG(Classifier baseClassifier, boolean concurrentTrain) Creates a new DDAG classifier to extend a binary classifier to handle multi-class problems.
MajorityVote(Classifier... voters) Creates a new Majority Vote classifier using the given voters.
OneVSAll(Classifier baseClassifier) Creates a new One VS All classifier.
OneVSAll(Classifier baseClassifier, boolean concurrentTraining) Creates a new One VS All classifier.
OneVSOne(Classifier baseClassifier) Creates a new One-vs-One classifier
OneVSOne(Classifier baseClassifier, boolean concurrentTrain) Creates a new One-vs-One classifier

Constructor parameters in jsat.classifiers with type arguments of type Classifier

Constructor and Description
MajorityVote(List<Classifier> voters) Creates a new Majority Vote classifier using the given voters.

Uses of Classifier in jsat.classifiers.bayesian

Classes in jsat.classifiers.bayesian that implement Classifier

Modifier and Type	Class and Description
class	AODE Averaged One-Dependence Estimators (AODE) is an extension of Naive Bayes that attempts to be more accurate by reducing the independence assumption.
class	BestClassDistribution BestClassDistribution is a generic class for performing classification by fitting a MultivariateDistribution to each class.
class	ConditionalProbabilityTable The conditional probability table (CPT) is a classifier for categorical attributes.
class	MultinomialNaiveBayes An implementation of the Multinomial Naive Bayes model (MNB).
class	MultivariateNormals This classifier can be seen as an extension of NaiveBayes.
class	NaiveBayes Provides an implementation of the Naive Bayes classifier that assumes numeric features come from some continuous probability distribution.
class	NaiveBayesUpdateable An implementation of Gaussian Naive Bayes that can be updated in an online fashion.
class	ODE One-Dependence Estimators (ODE) is an extension of Naive Bayes that, instead of assuming all features are independent, assumes all features are dependent on one other feature besides the target class.

Methods in jsat.classifiers.bayesian that return Classifier

Modifier and Type	Method and Description
Classifier	NaiveBayes.clone()
Classifier	ConditionalProbabilityTable.clone()
Classifier	BestClassDistribution.clone()

Uses of Classifier in jsat.classifiers.bayesian.graphicalmodel

Classes in jsat.classifiers.bayesian.graphicalmodel that implement Classifier

Modifier and Type	Class and Description
class	DiscreteBayesNetwork A class for representing a Baysian Network (BN) for discrete variables.
class	K2NetworkLearner An implementation of the K2 algorithm for learning the structure of a Bayesian Network.

Methods in jsat.classifiers.bayesian.graphicalmodel that return Classifier

Modifier and Type	Method and Description
Classifier	DiscreteBayesNetwork.clone()

Uses of Classifier in jsat.classifiers.boosting

Classes in jsat.classifiers.boosting that implement Classifier

Modifier and Type	Class and Description
class	AdaBoostM1 Implementation of Experiments with a New Boosting Algorithm, by Yoav Freund&Robert E.
class	AdaBoostM1PL An extension to the original AdaBoostM1 algorithm for parallel training.
class	ArcX4 Arc-x4 is a ensemble-classifier that performs re-weighting of the data points based on the total number of errors that have occurred for the data point.
class	Bagging An implementation of Bootstrap Aggregating, as described by LEO BREIMAN in "Bagging Predictors".
class	EmphasisBoost Emphasis Boost is a generalization of the Real AdaBoost algorithm, expanding the update term and providing the λ term to control the trade off.
class	LogitBoost An implementation of the original 2 class LogitBoost algorithm.
class	LogitBoostPL An extension to the original LogitBoost algorithm for parallel training.
class	ModestAdaBoost Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to reduce the generalization error and avoid over-fitting.
class	SAMME This is an implementation of the Multi-Class AdaBoost method SAMME (Stagewise Additive Modeling using a Multi-Class Exponential loss function), presented in Multi-class AdaBoost by Ji Zhu, Saharon Rosset, Hui Zou,&Trevor Hasstie This algorithm reduces to AdaBoostM1 for binary classification problems.
class	Stacking This provides an implementation of the Stacking ensemble method.
class	UpdatableStacking This provides an implementation of the Stacking ensemble method meant for Updatable models.
class	Wagging Wagging is a meta-classifier that is related to Bagging.
class	WaggingNormal Wagging using the Normal distribution.

Fields in jsat.classifiers.boosting with type parameters of type Classifier

Modifier and Type	Field and Description
protected List<Classifier>	ModestAdaBoost.hypoths The list of weak hypothesis
protected List<Classifier>	EmphasisBoost.hypoths The list of weak hypothesis
protected List<Classifier>	AdaBoostM1.hypoths The list of weak hypothesis

Methods in jsat.classifiers.boosting that return Classifier

Modifier and Type	Method and Description
Classifier	Wagging.getWeakClassifier() Returns the weak learner used for classification.
Classifier	ModestAdaBoost.getWeakLearner() Returns the weak learner currently being used by this method.
Classifier	EmphasisBoost.getWeakLearner() Returns the weak learner currently being used by this method.
Classifier	ArcX4.getWeakLearner() Returns the weak learner used
Classifier	AdaBoostM1.getWeakLearner() Returns the weak learner currently being used by this method.

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

Modifier and Type	Method and Description
List<Classifier>	SAMME.getModels()
List<Classifier>	ModestAdaBoost.getModels()
List<Classifier>	EmphasisBoost.getModels()
List<Classifier>	AdaBoostM1.getModels()

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

Modifier and Type	Method and Description
void	Wagging.setWeakLearner(Classifier weakL) Sets the weak learner used for classification.
void	ModestAdaBoost.setWeakLearner(Classifier weakLearner) Sets the weak learner used during training.
void	EmphasisBoost.setWeakLearner(Classifier weakLearner) Sets the weak learner used during training.
void	ArcX4.setWeakLearner(Classifier weakLearner) Sets the weak learner used at each iteration of learning
void	AdaBoostM1.setWeakLearner(Classifier weakLearner) Sets the weak learner used during training.

Constructors in jsat.classifiers.boosting with parameters of type Classifier

Constructor and Description
AdaBoostM1(Classifier weakLearner, int maxIterations)
AdaBoostM1PL(Classifier weakLearner, int maxIterations)
ArcX4(Classifier weakLearner, int iterations) Creates a new Arc-X4 classifier
Bagging(Classifier baseClassifier) Creates a new Bagger for classification.
Bagging(Classifier baseClassifier, int extraSamples, boolean simultaniousTraining) Creates a new Bagger for classification.
Bagging(Classifier baseClassifier, int extraSamples, boolean simultaniousTraining, int rounds, Random random) Creates a new Bagger for classification.
EmphasisBoost(Classifier weakLearner, int maxIterations, double lambda) Creates a new EmphasisBoost learner
ModestAdaBoost(Classifier weakLearner, int maxIterations) Creates a new ModestBoost learner
SAMME(Classifier weakLearner, int maxIterations)
Stacking(Classifier aggregatingClassifier, Classifier... baseClassifiers) Creates a new Stacking classifier that uses 3 folds of cross validation
Stacking(Classifier aggregatingClassifier, Classifier... baseClassifiers) Creates a new Stacking classifier that uses 3 folds of cross validation
Stacking(Classifier aggregatingClassifier, List<Classifier> baseClassifiers) Creates a new Stacking classifier that uses 3 folds of cross validation
Stacking(int folds, Classifier aggregatingClassifier, Classifier... baseClassifiers) Creates a new Stacking classifier
Stacking(int folds, Classifier aggregatingClassifier, Classifier... baseClassifiers) Creates a new Stacking classifier
Stacking(int folds, Classifier aggregatingClassifier, List<Classifier> baseClassifiers) Creates a new Stacking classifier
Wagging(ContinuousDistribution dist, Classifier weakL, int iterations) Creates a new Wagging classifier
WaggingNormal(Classifier weakLearner, int interations) Creates a new Wagging classifier

Constructor parameters in jsat.classifiers.boosting with type arguments of type Classifier

Constructor and Description
Stacking(Classifier aggregatingClassifier, List<Classifier> baseClassifiers) Creates a new Stacking classifier that uses 3 folds of cross validation
Stacking(int folds, Classifier aggregatingClassifier, List<Classifier> baseClassifiers) Creates a new Stacking classifier

Uses of Classifier in jsat.classifiers.calibration

Subinterfaces of Classifier in jsat.classifiers.calibration

Modifier and Type	Interface and Description
interface	BinaryScoreClassifier Many algorithms linear a binary separation between two classes A and B by representing the target labels with a -1 ad 1.

Classes in jsat.classifiers.calibration that implement Classifier

Modifier and Type	Class and Description
class	BinaryCalibration This abstract class provides the frame work for an algorithm to perform probability calibration based on the outputs of a base learning algorithm for binary classification problems.
class	IsotonicCalibration Isotonic Calibration is non-parametric, and only assumes that the underlying distribution from negative to positive examples is strictly a non-decreasing function.
class	PlattCalibration Platt Calibration essentially performs logistic regression on the output scores of a model against their class labels.

Uses of Classifier in jsat.classifiers.knn

Classes in jsat.classifiers.knn that implement Classifier

Modifier and Type	Class and Description
class	DANN DANN is an implementation of Discriminant Adaptive Nearest Neighbor.
class	LWL Locally Weighted Learner (LW) is the combined generalized implementation of Locally Weighted Regression (LWR) and Locally Weighted Naive Bayes (LWNB).
class	NearestNeighbour An implementation of the Nearest Neighbor algorithm, but with a British spelling! How fancy.

Methods in jsat.classifiers.knn that return Classifier

Modifier and Type	Method and Description
Classifier	DANN.clone()

Constructors in jsat.classifiers.knn with parameters of type Classifier

Constructor and Description
LWL(Classifier classifier, int k, DistanceMetric dm) Creates a new LWL classifier
LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf) Creates a new LWL classifier
LWL(Classifier classifier, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf) Creates a new LWL classifier

Uses of Classifier in jsat.classifiers.linear

Classes in jsat.classifiers.linear that implement Classifier

Modifier and Type	Class and Description
class	ALMA2 Provides a linear implementation of the ALMAp algorithm for p = 2, which is considerably more efficient to compute.
class	AROW An implementation of Adaptive Regularization of Weight Vectors (AROW), which uses second order information to learn a large margin binary classifier.
class	BBR This is an implementation of Bayesian Binary Regression for L1 and L2 regularized logistic regression.
class	LinearBatch LinearBatch learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
class	LinearL1SCD Implements an iterative and single threaded form of fast Stochastic Coordinate Decent for optimizing L1 regularized linear regression problems.
class	LinearSGD LinearSGD learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
class	LogisticRegressionDCD This provides an implementation of regularized logistic regression using Dual Coordinate Descent.
class	NewGLMNET NewGLMNET is a batch method for solving Elastic Net regularized Logistic Regression problems of the form 0.5 * (1-α) ||w||2 + α * ||w||1 + C * ∑Ni=1 ℓ (wT xi + b, yi).
class	NHERD Implementation of the Normal Herd (NHERD) algorithm for learning a linear binary classifier.
class	PassiveAggressive An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression.
class	ROMMA Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs.
class	SCD Implementation of Stochastic Coordinate Descent for L1 regularized classification and regression.
class	SCW Provides an Implementation of Confidence-Weighted (CW) learning and Soft Confidence-Weighted (SCW), both of which are binary linear classifiers inspired by PassiveAggressive.
class	SMIDAS Implements the iterative and single threaded stochastic solver for L1 regularized linear regression problems SMIDAS (Stochastic Mirror Descent Algorithm mAde Sparse).
class	SPA Support class Passive Aggressive (SPA) is a multi class generalization of PassiveAggressive.
class	STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.
class	StochasticMultinomialLogisticRegression This is a Stochastic implementation of Multinomial Logistic Regression.
class	StochasticSTLinearL1 This base class provides shared functionality and variables used by two different training algorithms for L1 regularized linear models.

Methods in jsat.classifiers.linear that return Classifier

Modifier and Type	Method and Description
Classifier	StochasticMultinomialLogisticRegression.clone()
Classifier	LogisticRegressionDCD.clone()

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

Modifier and Type	Method and Description
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)

Uses of Classifier in jsat.classifiers.linear.kernelized

Classes in jsat.classifiers.linear.kernelized that implement Classifier

Modifier and Type	Class and Description
class	ALMA2K Provides a kernelized version of the ALMA2 algorithm.
class	BOGD Bounded Online Gradient Descent (BOGD) is a kernel learning algorithm that uses a bounded number of support vectors.
class	CSKLR An implementation of Conservative Stochastic Kernel Logistic Regression.
class	CSKLRBatch An implementation of Conservative Stochastic Kernel Logistic Regression.
class	DUOL Provides an implementation of Double Update Online Learning (DUOL) algorithm.
class	Forgetron Implementation of the first two Forgetron algorithms.
class	KernelSGD Kernel SGD is the kernelized counterpart to LinearSGD, and learns nonlinear functions via the kernel trick.
class	OSKL Online Sparse Kernel Learning by Sampling and Smooth Losses (OSKL) is an online algorithm for learning sparse kernelized solutions to binary classification problems.
class	Projectron An implementation of the Projectron and Projectrion++ algorithms.

Uses of Classifier in jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork that implement Classifier

Modifier and Type	Class and Description
class	BackPropagationNet An implementation of a Feed Forward Neural Network (NN) trained by Back Propagation.
class	DReDNetSimple This class provides a neural network based on Geoffrey Hinton's Deep Rectified Dropout Nets.
class	LVQ Learning Vector Quantization (LVQ) is an algorithm that extends SOM to take advantage of label information to perform classification.
class	LVQLLC LVQ with Locally Learned Classifier (LVQ-LLC) is an adaption of the LVQ algorithm I have come up with.
class	Perceptron The perceptron is a simple algorithm that attempts to find a hyperplane that separates two classes.
class	RBFNet This provides a highly configurable implementation of a Radial Basis Function Neural Network.
class	SOM An implementation of a Self Organizing Map, also called a Kohonen Map.

Methods in jsat.classifiers.neuralnetwork that return Classifier

Modifier and Type	Method and Description
Classifier	LVQLLC.getLocalClassifier() Returns the classifier used for each prototype

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

Modifier and Type	Method and Description
void	LVQLLC.setLocalClassifier(Classifier localClassifier) Each prototype will create a classifier that is local to itself, and trained on the points that belong to the prototype and those near the border of the prototype.

Constructors in jsat.classifiers.neuralnetwork with parameters of type Classifier

Constructor and Description
LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier) Creates a new LVQ-LLC instance
LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier, double learningRate, int representativesPerClass) Creates a new LVQ-LLC instance
LVQLLC(DistanceMetric dm, int iterations, Classifier localClasifier, double learningRate, int representativesPerClass, LVQ.LVQVersion lvqVersion, DecayRate learningDecay) Creates a new LVQ-LLC instance
RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Classifier baseClassifier) Creates a new RBF Network for classification tasks.

Uses of Classifier in jsat.classifiers.svm

Classes in jsat.classifiers.svm that implement Classifier

Modifier and Type	Class and Description
class	DCD Implements Dual Coordinate Descent (DCD) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	DCDs Implements Dual Coordinate Descent with shrinking (DCDs) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	DCSVM This is an implementation of the Divide-and-Conquer Support Vector Machine (DC-SVM).
class	LSSVM The Least Squares Support Vector Machine (LS-SVM) is an alternative to the standard SVM classifier for regression and binary classification problems.
class	Pegasos Implements the linear kernel mini-batch version of the Pegasos SVM classifier.
class	PegasosK Implements the kernelized version of the Pegasos algorithm for SVMs.
class	PlattSMO An implementation of SVMs using Platt's Sequential Minimum Optimization (SMO) for both Classification and Regression problems.
class	SBP Implementation of the Stochastic Batch Perceptron (SBP) algorithm.
class	SVMnoBias This class implements a version of the Support Vector Machine without a bias term.

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

Modifier and Type	Method and Description
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)

Uses of Classifier in jsat.classifiers.svm.extended

Classes in jsat.classifiers.svm.extended that implement Classifier

Modifier and Type	Class and Description
class	AMM This is the batch variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm.
class	CPM This class implements the Convex Polytope Machine (CPM), which is an extension of the Linear SVM.
class	OnlineAMM This is the Online variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm.

Uses of Classifier in jsat.classifiers.trees

Classes in jsat.classifiers.trees that implement Classifier

Modifier and Type	Class and Description
class	DecisionStump This class is a 1-rule.
class	DecisionTree Creates a decision tree from DecisionStumps.
class	ERTrees Extra Randomized Trees (ERTrees) is an ensemble method built on top of ExtraTree.
class	ExtraTree The ExtraTree is an Extremely Randomized Tree.
class	ID3
class	RandomDecisionTree An extension of Decision Trees, it ignores the given set of features to use- and selects a new random subset of features at each node for use.
class	RandomForest Random Forest is an extension of Bagging that is applied only to DecisionTrees.

Methods in jsat.classifiers.trees that return Classifier

Modifier and Type	Method and Description
Classifier	ID3.clone()

Uses of Classifier in jsat.datatransform

Classes in jsat.datatransform that implement Classifier

Modifier and Type	Class and Description
class	DataModelPipeline A Data Model Pipeline combines several data transforms and a base Classifier or Regressor into a unified object for performing classification and Regression with.

Constructors in jsat.datatransform with parameters of type Classifier

Constructor and Description
DataModelPipeline(Classifier baseClassifier, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataModelPipeline(Classifier baseClassifier, DataTransformProcess dtp) Creates a new Data Model Pipeline from the given transform process and base classifier

Uses of Classifier in jsat.datatransform.featureselection

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

Constructor and Description
BDS(int featureCount, ClassificationDataSet dataSet, Classifier evaluator, int folds) Performs BDS feature selection for a classification problem
BDS(int featureCount, Classifier evaluator, int folds) Creates a 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
LRS(int L, int R, Classifier evaluater, int folds) Creates a LRS feature selection object for a classification problem
SBS(int minFeatures, int maxFeatures, ClassificationDataSet cds, Classifier evaluater, int folds, double maxDecrease) Performs SBS feature selection for a classification problem
SBS(int minFeatures, int maxFeatures, Classifier evaluater, 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
SFS(int minFeatures, int maxFeatures, Classifier evaluater, double maxIncrease) Performs SFS feature selection for a classification problem

Uses of Classifier in jsat.parameters

Classes in jsat.parameters that implement Classifier

Modifier and Type	Class and Description
class	GridSearch GridSearch is a simple method for tuning the parameters of a classification or regression algorithm.
class	ModelSearch This abstract class provides boilerplate for algorithms that search a model's parameter space to find the parameters that provide the best overall performance.
class	RandomSearch Random Search is a simple method for tuning the parameters of a classification or regression algorithm.

Fields in jsat.parameters declared as Classifier

Modifier and Type	Field and Description
protected Classifier	ModelSearch.baseClassifier
protected Classifier	ModelSearch.trainedClassifier

Methods in jsat.parameters that return Classifier

Modifier and Type	Method and Description
Classifier	ModelSearch.getBaseClassifier() Returns the base classifier that was originally passed in when constructing this GridSearch.
Classifier	ModelSearch.getTrainedClassifier() Returns the resultant classifier trained on the whole data set after performing parameter tuning.

Constructors in jsat.parameters with parameters of type Classifier

Constructor and Description
GridSearch(Classifier baseClassifier, int folds) Creates a new GridSearch to tune the specified parameters of a classification model.
ModelSearch(Classifier baseClassifier, int folds)
RandomSearch(Classifier baseClassifier, int folds) Creates a new GridSearch to tune the specified parameters of a classification model.

Uses of Classifier in jsat.regression

Classes in jsat.regression that implement Classifier

Modifier and Type	Class and Description
class	LogisticRegression Logistic regression is a common method used to fit a probability between binary outputs.