Uses of Class
weka.classifiers.Classifier

Packages that use Classifier

weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.evaluation
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.ft
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	ClassifierSubsetEval.getClassifier() Get the classifier used as the base learner.
Classifier	WrapperSubsetEval.getClassifier() Get the classifier used as the base learner.

Methods in weka.attributeSelection with parameters of type Classifier

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

Uses of Classifier in weka.classifiers

Subclasses of Classifier in weka.classifiers

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	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	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	Classifier.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[]	Classifier.makeCopies(Classifier model, int num) Creates a given number of deep copies of the given classifier using serialization.
static Classifier	Classifier.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 (if the class is nominal).
static Classifier[]	Classifier.makeCopies(Classifier model, int num) Creates a given number of deep copies of the given classifier using serialization.
static Classifier	Classifier.makeCopy(Classifier model) Creates a deep copy of the given classifier using serialization.
static void	Evaluation.printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text) Prints the predictions for the given dataset into a supplied StringBuffer
static void	Evaluation.printClassifications(Classifier classifier, Instances train, ConverterUtils.DataSource testSource, int classIndex, Range attributesToOutput, StringBuffer predsText) Prints the predictions for the given dataset into a String variable.
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

Subclasses of Classifier in weka.classifiers.bayes

class	AODE AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
class	AODEsr AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value. For more information, see: Fei Zheng, Geoffrey I.
class	BayesianLogisticRegression Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D.
class	BayesNet Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	ComplementNaiveBayes Class for building and using a Complement class Naive Bayes classifier. For more information see, Jason D.
class	DMNBtext Class for building and using a Discriminative Multinomial Naive Bayes classifier.
class	HNB Contructs Hidden Naive Bayes classification model with high classification accuracy and AUC. For more information refer to: H.
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesMultinomialUpdateable Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesSimple Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution. For more information, see Richard Duda, Peter Hart (1973).
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.
class	WAODE WAODE contructs the model called Weightily Averaged One-Dependence Estimators. For more information, see L.

Uses of Classifier in weka.classifiers.bayes.net

Subclasses of Classifier in weka.classifiers.bayes.net

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.functions

Subclasses of Classifier in weka.classifiers.functions

class	GaussianProcesses Implements Gaussian Processes for regression without hyperparameter-tuning.
class	IsotonicRegression Learns an isotonic regression model.
class	LeastMedSq Implements a least median sqaured linear regression utilising the existing weka LinearRegression class to form predictions.
class	LibLINEAR A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008).
class	LibSVM A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool.
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	PaceRegression Class for building pace regression linear models and using them for prediction.
class	PLSClassifier A wrapper classifier for the PLSFilter, utilizing the PLSFilter's ability to perform predictions.
class	RBFNetwork Class that implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that.
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	SPegasos Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al.
class	VotedPerceptron Implementation of the voted perceptron algorithm by Freund and Schapire.
class	Winnow Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N.

Uses of Classifier in weka.classifiers.lazy

Subclasses of Classifier in weka.classifiers.lazy

class	IB1 Nearest-neighbour 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	LBR Lazy Bayesian Rules Classifier.
class	LWL Locally weighted learning.

Uses of Classifier in weka.classifiers.meta

Subclasses of Classifier in weka.classifiers.meta

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	ClassificationViaClustering A simple meta-classifier that uses a clusterer for classification.
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	Dagging This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	FilteredClassifier Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	Grading Implements Grading.
class	GridSearch Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
class	LogitBoost Class for performing additive logistic regression.
class	MetaCost This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
class	MultiClassClassifier 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	OrdinalClassClassifier Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
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	RotationForest Class for construction a Rotation Forest.
class	Stacking Combines several classifiers using the stacking method.
class	StackingC Implements StackingC (more efficient version of stacking). For more information, see A.K.
class	ThresholdSelector A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
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	GridSearch.getBestClassifier() returns the best Classifier setup
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	GridSearch.setClassifier(Classifier newClassifier) Set the base learner.
void	RacedIncrementalLogitBoost.setClassifier(Classifier newClassifier) Set the base learner.
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.meta.nestedDichotomies

Subclasses of Classifier in weka.classifiers.meta.nestedDichotomies

class	ClassBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	DataNearBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	ND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.

Uses of Classifier in weka.classifiers.mi

Subclasses of Classifier in weka.classifiers.mi

class	CitationKNN Modified version of the Citation kNN multi instance classifier. For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
class	MDD Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998).
class	MIBoost MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. For more information about Adaboost, see: Yoav Freund, Robert E.
class	MIDD Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998).
class	MIEMDD EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
class	MILR Uses either standard or collective multi-instance assumption, but within linear regression.
class	MINND Multiple-Instance Nearest Neighbour with Distribution learner. It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
class	MIOptimalBall This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
class	MISMO 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	MISVM Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
class	MIWrapper A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E.
class	SimpleMI Reduces MI data into mono-instance data.

Uses of Classifier in weka.classifiers.misc

Subclasses of Classifier in weka.classifiers.misc

class	HyperPipes Class implementing a HyperPipe classifier.
class	SerializedClassifier A wrapper around a serialized classifier model.
class	VFI Classification by voting feature intervals.

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

Subclasses of Classifier in weka.classifiers.pmml.consumer

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.

Uses of Classifier in weka.classifiers.rules

Subclasses of Classifier in weka.classifiers.rules

class	ConjunctiveRule This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression.
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	DTNB Class for building and using a decision table/naive bayes hybrid classifier.
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	NNge Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).
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	Prism Class for building and using a PRISM rule set for classification.
class	Ridor An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate.
class	ZeroR Class for building and using a 0-R classifier.

Uses of Classifier in weka.classifiers.trees

Subclasses of Classifier in weka.classifiers.trees

class	ADTree Class for generating an alternating decision tree.
class	BFTree Class for building a best-first decision tree classifier.
class	DecisionStump Class for building and using a decision stump.
class	FT Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
class	Id3 Class for constructing an unpruned decision tree based on the ID3 algorithm.
class	J48 Class for generating a pruned or unpruned C4.5 decision tree.
class	J48graft Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
class	LADTree Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
class	LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P M5Base.
class	NBTree Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
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.
class	SimpleCart Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H.
class	UserClassifier Interactively classify through visual means.

Uses of Classifier in weka.classifiers.trees.ft

Subclasses of Classifier in weka.classifiers.trees.ft

class	FTInnerNode Class for Functional Inner tree structure.
class	FTLeavesNode Class for Functional Leaves tree version.
class	FTNode Class for Functional tree structure.
class	FTtree Abstract class for Functional tree structure.

Uses of Classifier in weka.classifiers.trees.lmt

Subclasses of Classifier in weka.classifiers.trees.lmt

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

Subclasses of Classifier in weka.classifiers.trees.m5

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 classifier currently set for this wrapper
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 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 with parameters of type Classifier

static void

ClassifierPanel.processClassifierPrediction(Instance toPredict, Classifier classifier, Evaluation eval, Instances plotInstances, FastVector plotShape, FastVector plotSize) Process a classifier's prediction for an instance and update a set of plotting instances and additional plotting info.