jsat.classifiers

Interface Classifier

All Superinterfaces:

Cloneable, Serializable

All Known Subinterfaces:

BinaryScoreClassifier, UpdateableClassifier, WarmClassifier

All Known Implementing Classes:

AdaBoostM1, AdaBoostM1PL, ALMA2, ALMA2K, AMM, AODE, ArcX4, AROW, BackPropagationNet, Bagging, BaseUpdateableClassifier, BBR, BestClassDistribution, BinaryCalibration, BOGD, ConditionalProbabilityTable, CPM, CSKLR, CSKLRBatch, DANN, DataModelPipeline, DCD, DCDs, DCSVM, DDAG, DecisionStump, DecisionTree, DiscreteBayesNetwork, DReDNetSimple, DUOL, EmphasisBoost, ERTrees, ExtraTree, Forgetron, GridSearch, ID3, IsotonicCalibration, K2NetworkLearner, KernelSGD, LinearBatch, LinearL1SCD, LinearSGD, LogisticRegression, LogisticRegressionDCD, LogitBoost, LogitBoostPL, LSSVM, LVQ, LVQLLC, LWL, MajorityVote, ModelSearch, ModestAdaBoost, MultinomialLogisticRegression, MultinomialNaiveBayes, MultivariateNormals, NaiveBayes, NaiveBayesUpdateable, NearestNeighbour, NewGLMNET, NHERD, ODE, OneVSAll, OneVSOne, OnlineAMM, OSKL, PassiveAggressive, Pegasos, PegasosK, Perceptron, PlattCalibration, PlattSMO, PriorClassifier, Projectron, RandomDecisionTree, RandomForest, RandomSearch, RBFNet, RegressorToClassifier, Rocchio, ROMMA, SAMME, SBP, SCD, SCW, SMIDAS, SOM, SPA, Stacking, STGD, StochasticMultinomialLogisticRegression, StochasticSTLinearL1, SVMnoBias, UpdatableStacking, Wagging, WaggingNormal

public interface Classifier
extends Cloneable, Serializable

A Classifier is used to predict the target class of new unseen data points.

Author:

Edward Raff

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
Classifier	clone()
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	trainC(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	trainC(ClassificationDataSet dataSet, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.

Method Detail

classify

CategoricalResults classify(DataPoint data)

Performs classification on the given data point.

Parameters:

data - the data point to classify

Returns:

the results of the classification.

Throws:

UntrainedModelException - if the method is called before the model has been trained

ModelMismatchException - if the given data point is incompatible with the model

trainC

void trainC(ClassificationDataSet dataSet,
            ExecutorService threadPool)

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Parameters:

dataSet - the data set to train on

threadPool - the source of threads to use.

Throws:

FailedToFitException - if the model is unable to be constructed for some reason

trainC

void trainC(ClassificationDataSet dataSet)

Trains the classifier and constructs a model for classification using the given data set.

Parameters:

dataSet - the data set to train on

Throws:

FailedToFitException - if the model is unable to be constructed for some reason

supportsWeightedData

boolean supportsWeightedData()

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Returns:

true if the model supports weighted data, false otherwise

clone

Classifier clone()