Uses of Interface
weka.classifiers.Classifier

Packages that use Classifier

weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.evaluation
weka.classifiers.evaluation.output.prediction
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.classifiers.trees.m5
weka.experiment
weka.filters.supervised.attribute
weka.filters.unsupervised.instance
weka.gui.beans
weka.gui.boundaryvisualizer
weka.gui.explorer

Uses of Classifier in weka.attributeSelection

Methods in weka.attributeSelection that return Classifier

Classifier

WrapperSubsetEval.getClassifier() Get the classifier used as the base learner.

Methods in weka.attributeSelection with parameters of type Classifier

void	WrapperSubsetEval.setClassifier(Classifier newClassifier) Set the classifier to use for accuracy estimation

Uses of Classifier in weka.classifiers

Classes in weka.classifiers that implement Classifier

class	AbstractClassifier Abstract classifier.
class	IteratedSingleClassifierEnhancer Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
class	MultipleClassifiersCombiner Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
class	ParallelIteratedSingleClassifierEnhancer Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.
class	ParallelMultipleClassifiersCombiner Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers.
class	RandomizableClassifier Abstract utility class for handling settings common to randomizable classifiers.
class	RandomizableIteratedSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	RandomizableMultipleClassifiersCombiner Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
class	RandomizableParallelIteratedSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.
class	RandomizableParallelMultipleClassifiersCombiner Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers based on a given random number seed.
class	RandomizableSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	SingleClassifierEnhancer Abstract utility class for handling settings common to meta classifiers that use a single base learner.

Methods in weka.classifiers that return Classifier

static Classifier	AbstractClassifier.forName(String classifierName, String[] options) Creates a new instance of a classifier given it's class name and (optional) arguments to pass to it's setOptions method.
Classifier	CheckClassifier.getClassifier() Get the classifier used as the classifier
Classifier	SingleClassifierEnhancer.getClassifier() Get the classifier used as the base learner.
Classifier	BVDecomposeSegCVSub.getClassifier() Gets the name of the classifier being analysed
Classifier	BVDecompose.getClassifier() Gets the name of the classifier being analysed
Classifier	CheckSource.getClassifier() Gets the classifier being used for the tests, can be null.
Classifier	MultipleClassifiersCombiner.getClassifier(int index) Gets a single classifier from the set of available classifiers.
Classifier[]	MultipleClassifiersCombiner.getClassifiers() Gets the list of possible classifers to choose from.
Classifier	CheckSource.getSourceCode() Gets the class to test.
static Classifier[]	AbstractClassifier.makeCopies(Classifier model, int num) Creates a given number of deep copies of the given classifier using serialization.
static Classifier	AbstractClassifier.makeCopy(Classifier model) Creates a deep copy of the given classifier using serialization.

Methods in weka.classifiers with parameters of type Classifier

void	Evaluation.crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting) Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
double[]	Evaluation.evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting) Evaluates the classifier on a given set of instances.
static String	Evaluation.evaluateModel(Classifier classifier, String[] options) Evaluates a classifier with the options given in an array of strings.
double	Evaluation.evaluateModelOnce(Classifier classifier, Instance instance) Evaluates the classifier on a single instance.
double	Evaluation.evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) Evaluates the classifier on a single instance and records the prediction.
static Classifier[]	AbstractClassifier.makeCopies(Classifier model, int num) Creates a given number of deep copies of the given classifier using serialization.
static Classifier	AbstractClassifier.makeCopy(Classifier model) Creates a deep copy of the given classifier using serialization.
static void	AbstractClassifier.runClassifier(Classifier classifier, String[] options) runs the classifier instance with the given options.
void	CheckClassifier.setClassifier(Classifier newClassifier) Set the classifier for boosting.
void	SingleClassifierEnhancer.setClassifier(Classifier newClassifier) Set the base learner.
void	BVDecomposeSegCVSub.setClassifier(Classifier newClassifier) Set the classifiers being analysed
void	BVDecompose.setClassifier(Classifier newClassifier) Set the classifiers being analysed
void	CheckSource.setClassifier(Classifier value) Sets the classifier to use for the comparison.
void	MultipleClassifiersCombiner.setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
void	CheckSource.setSourceCode(Classifier value) Sets the class to test.

Uses of Classifier in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement Classifier

class	BayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesMultinomialText Multinomial naive bayes for text data.
class	NaiveBayesMultinomialUpdateable Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

Uses of Classifier in weka.classifiers.bayes.net

Classes in weka.classifiers.bayes.net that implement Classifier

class	BayesNetGenerator Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	BIFReader Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format. For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
class	EditableBayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.

Uses of Classifier in weka.classifiers.evaluation

Methods in weka.classifiers.evaluation with parameters of type Classifier

FastVector	EvaluationUtils.getCVPredictions(Classifier classifier, Instances data, int numFolds) Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.
Prediction	EvaluationUtils.getPrediction(Classifier classifier, Instance test) Generate a single prediction for a test instance given the pre-trained classifier.
FastVector	EvaluationUtils.getTestPredictions(Classifier classifier, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.
FastVector	EvaluationUtils.getTrainTestPredictions(Classifier classifier, Instances train, Instances test) Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.

Uses of Classifier in weka.classifiers.evaluation.output.prediction

Methods in weka.classifiers.evaluation.output.prediction with parameters of type Classifier

void	AbstractOutput.print(Classifier classifier, ConverterUtils.DataSource testset) Prints the header, classifications and footer to the buffer.
void	AbstractOutput.print(Classifier classifier, Instances testset) Prints the header, classifications and footer to the buffer.
void	AbstractOutput.printClassification(Classifier classifier, Instance inst, int index) Prints the classification to the buffer.
void	AbstractOutput.printClassifications(Classifier classifier, ConverterUtils.DataSource testset) Prints the classifications to the buffer.
void	AbstractOutput.printClassifications(Classifier classifier, Instances testset) Prints the classifications to the buffer.

Uses of Classifier in weka.classifiers.functions

Classes in weka.classifiers.functions that implement Classifier

class	GaussianProcesses Implements Gaussian processes for regression without hyperparameter-tuning.
class	LinearRegression Class for using linear regression for prediction.
class	Logistic Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
class	MultilayerPerceptron A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both.
class	SGD Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).
class	SGDText Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
class	SimpleLinearRegression Learns a simple linear regression model.
class	SimpleLogistic Classifier for building linear logistic regression models.
class	SMO Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	SMOreg SMOreg implements the support vector machine for regression.
class	VotedPerceptron Implementation of the voted perceptron algorithm by Freund and Schapire.

Uses of Classifier in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement Classifier

class	IBk K-nearest neighbours classifier.
class	KStar K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
class	LWL Locally weighted learning.

Uses of Classifier in weka.classifiers.meta

Classes in weka.classifiers.meta that implement Classifier

class	AdaBoostM1 Class for boosting a nominal class classifier using the Adaboost M1 method.
class	AdditiveRegression Meta classifier that enhances the performance of a regression base classifier.
class	AttributeSelectedClassifier Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
class	Bagging Class for bagging a classifier to reduce variance.
class	ClassificationViaRegression Class for doing classification using regression methods.
class	CostSensitiveClassifier A metaclassifier that makes its base classifier cost-sensitive.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
class	FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	LogitBoost Class for performing additive logistic regression.
class	MultiClassClassifier A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiClassClassifierUpdateable A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiScheme Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
class	RandomCommittee Class for building an ensemble of randomizable base classifiers.
class	RandomSubSpace This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
class	RegressionByDiscretization A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
class	Stacking Combines several classifiers using the stacking method.
class	Vote Class for combining classifiers.

Methods in weka.classifiers.meta that return Classifier

Classifier[][]	LogitBoost.classifiers() Returns the array of classifiers that have been built.
Classifier	MultiScheme.getClassifier(int index) Gets a single classifier from the set of available classifiers.
Classifier[]	MultiScheme.getClassifiers() Gets the list of possible classifers to choose from.
Classifier	Stacking.getMetaClassifier() Gets the meta classifier.

Methods in weka.classifiers.meta with parameters of type Classifier

void	MultiScheme.setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
void	Stacking.setMetaClassifier(Classifier classifier) Adds meta classifier

Constructors in weka.classifiers.meta with parameters of type Classifier

AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.

Uses of Classifier in weka.classifiers.misc

Classes in weka.classifiers.misc that implement Classifier

class	InputMappedClassifier Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure.
class	SerializedClassifier A wrapper around a serialized classifier model.

Methods in weka.classifiers.misc that return Classifier

Classifier

SerializedClassifier.getCurrentModel() Gets the currently loaded model (can be null).

Methods in weka.classifiers.misc with parameters of type Classifier

void	SerializedClassifier.setModel(Classifier value) Sets the fully built model to use, if one doesn't want to load a model from a file or already deserialized a model from somewhere else.

Uses of Classifier in weka.classifiers.pmml.consumer

Classes in weka.classifiers.pmml.consumer that implement Classifier

class	GeneralRegression Class implementing import of PMML General Regression model.
class	NeuralNetwork Class implementing import of PMML Neural Network model.
class	PMMLClassifier Abstract base class for all PMML classifiers.
class	Regression Class implementing import of PMML Regression model.
class	RuleSetModel Class implementing import of PMML RuleSetModel.
class	SupportVectorMachineModel Implements a PMML SupportVectorMachineModel
class	TreeModel Class implementing import of PMML TreeModel.

Uses of Classifier in weka.classifiers.rules

Classes in weka.classifiers.rules that implement Classifier

class	DecisionTable Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables.
class	JRip This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
class	M5Rules Generates a decision list for regression problems using separate-and-conquer.
class	OneR Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
class	PART Class for generating a PART decision list.
class	ZeroR Class for building and using a 0-R classifier.

Uses of Classifier in weka.classifiers.trees

Classes in weka.classifiers.trees that implement Classifier

class	DecisionStump Class for building and using a decision stump.
class	J48 Class for generating a pruned or unpruned C4.5 decision tree.
class	LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P M5Base.
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).
class	RandomTree Class for constructing a tree that considers K randomly chosen attributes at each node.
class	REPTree Fast decision tree learner.

Uses of Classifier in weka.classifiers.trees.lmt

Classes in weka.classifiers.trees.lmt that implement Classifier

class	LMTNode Class for logistic model tree structure.
class	LogisticBase Base/helper class for building logistic regression models with the LogitBoost algorithm.

Uses of Classifier in weka.classifiers.trees.m5

Classes in weka.classifiers.trees.m5 that implement Classifier

class	M5Base M5Base.
class	PreConstructedLinearModel This class encapsulates a linear regression function.
class	RuleNode Constructs a node for use in an m5 tree or rule

Uses of Classifier in weka.experiment

Methods in weka.experiment that return Classifier

Classifier	RegressionSplitEvaluator.getClassifier() Get the value of Classifier.
Classifier	ClassifierSplitEvaluator.getClassifier() Get the value of Classifier.

Methods in weka.experiment with parameters of type Classifier

void	RegressionSplitEvaluator.setClassifier(Classifier newClassifier) Sets the classifier.
void	ClassifierSplitEvaluator.setClassifier(Classifier newClassifier) Sets the classifier.

Uses of Classifier in weka.filters.supervised.attribute

Methods in weka.filters.supervised.attribute that return Classifier

Classifier

AddClassification.getClassifier() Gets the classifier used by the filter.

Methods in weka.filters.supervised.attribute with parameters of type Classifier

void	AddClassification.setClassifier(Classifier value) Sets the classifier to classify instances with.

Uses of Classifier in weka.filters.unsupervised.instance

Methods in weka.filters.unsupervised.instance that return Classifier

Classifier

RemoveMisclassified.getClassifier() Gets the classifier used by the filter.

Methods in weka.filters.unsupervised.instance with parameters of type Classifier

void	RemoveMisclassified.setClassifier(Classifier classifier) Sets the classifier to classify instances with.

Uses of Classifier in weka.gui.beans

Methods in weka.gui.beans that return Classifier

Classifier	Classifier.getClassifier() Get the currently trained classifier.
Classifier	BatchClassifierEvent.getClassifier() Get the classifier
Classifier	IncrementalClassifierEvent.getClassifier() Get the classifier
Classifier	Classifier.getClassifierTemplate() Return the classifier template currently in use.

Methods in weka.gui.beans with parameters of type Classifier

void	BatchClassifierEvent.setClassifier(Classifier classifier) Set the classifier
void	IncrementalClassifierEvent.setClassifier(Classifier c)
void	Classifier.setClassifierTemplate(Classifier c) Set the template classifier for this wrapper

Constructors in weka.gui.beans with parameters of type Classifier

BatchClassifierEvent(Object source, Classifier scheme, DataSetEvent trsI, DataSetEvent tstI, int setNum, int maxSetNum)
Creates a new BatchClassifierEvent instance.

BatchClassifierEvent(Object source, Classifier scheme, DataSetEvent trsI, DataSetEvent tstI, int runNum, int maxRunNum, int setNum, int maxSetNum)
Creates a new BatchClassifierEvent instance.

IncrementalClassifierEvent(Object source, Classifier scheme, Instance currentI, int status)
Creates a new IncrementalClassifierEvent instance.

IncrementalClassifierEvent(Object source, Classifier scheme, Instances structure)
Creates a new incremental classifier event that encapsulates header information and classifier.

Uses of Classifier in weka.gui.boundaryvisualizer

Methods in weka.gui.boundaryvisualizer with parameters of type Classifier

static void	BoundaryVisualizer.createNewVisualizerWindow(Classifier classifier, Instances instances) Creates a new GUI window with all of the BoundaryVisualizer trappings,
void	BoundaryVisualizer.setClassifier(Classifier newClassifier) Set a classifier to use
void	RemoteBoundaryVisualizerSubTask.setClassifier(Classifier dc) Set the classifier to use
void	BoundaryPanel.setClassifier(Classifier classifier) Set the classifier to use.

Uses of Classifier in weka.gui.explorer

Methods in weka.gui.explorer that return Classifier

Classifier

ClassifierErrorsPlotInstances.getClassifier() Returns the currently set classifier.

Methods in weka.gui.explorer with parameters of type Classifier

void	ClassifierErrorsPlotInstances.process(Instance toPredict, Classifier classifier, Evaluation eval) Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.
void	ClassifierErrorsPlotInstances.setClassifier(Classifier value) Sets the classifier used for making the predictions.