Uses of Interface
weka.core.OptionHandler

Packages that use OptionHandler

Package	Description
weka.associations
weka.attributeSelection
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.bayes.net.estimate
weka.classifiers.bayes.net.search
weka.classifiers.bayes.net.search.ci
weka.classifiers.bayes.net.search.fixed
weka.classifiers.bayes.net.search.global
weka.classifiers.bayes.net.search.local
weka.classifiers.evaluation.output.prediction
weka.classifiers.functions
weka.classifiers.functions.supportVector
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.clusterers
weka.core
weka.core.converters
weka.core.neighboursearch
weka.core.neighboursearch.balltrees
weka.core.neighboursearch.kdtrees
weka.core.stemmers
weka.core.stopwords
weka.core.tokenizers
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.estimators
weka.experiment
weka.filters
weka.filters.supervised.attribute
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance
weka.gui
weka.gui.explorer
weka.gui.scripting

Uses of OptionHandler in weka.associations

Subinterfaces of OptionHandler in weka.associations

Modifier and Type	Interface	Description
interface	CARuleMiner	Interface for learning class association rules.

Classes in weka.associations that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractAssociator	Abstract scheme for learning associations.
class	Apriori	Class implementing an Apriori-type algorithm.
class	CheckAssociator	Class for examining the capabilities and finding problems with associators.
class	FilteredAssociator	Class for running an arbitrary associator on data that has been passed through an arbitrary filter.
class	FPGrowth	Class implementing the FP-growth algorithm for finding large item sets without candidate generation.
class	SingleAssociatorEnhancer	Abstract utility class for handling settings common to meta associators that use a single base associator.

Uses of OptionHandler in weka.attributeSelection

Classes in weka.attributeSelection that implement OptionHandler

Modifier and Type	Class	Description
class	ASEvaluation	Abstract attribute selection evaluation class
class	ASSearch	Abstract attribute selection search class.
class	AttributeSetEvaluator	Abstract attribute set evaluator.
class	BestFirst	BestFirst: Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
class	CfsSubsetEval	CfsSubsetEval : Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred. For more information see: M.
class	CheckAttributeSelection	Class for examining the capabilities and finding problems with attribute selection schemes.
class	ClassifierAttributeEval	ClassifierAttributeEval : Evaluates the worth of an attribute by using a user-specified classifier.
class	ClassifierSubsetEval	Classifier subset evaluator: Evaluates attribute subsets on training data or a separate hold out testing set.
class	CorrelationAttributeEval	CorrelationAttributeEval : Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class. Nominal attributes are considered on a value by value basis by treating each value as an indicator.
class	GainRatioAttributeEval	GainRatioAttributeEval : Evaluates the worth of an attribute by measuring the gain ratio with respect to the class. GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).
class	GreedyStepwise	GreedyStepwise : Performs a greedy forward or backward search through the space of attribute subsets.
class	HoldOutSubsetEvaluator	Abstract attribute subset evaluator capable of evaluating subsets with respect to a data set that is distinct from that used to initialize/ train the subset evaluator.
class	InfoGainAttributeEval	InfoGainAttributeEval : Evaluates the worth of an attribute by measuring the information gain with respect to the class. InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).
class	OneRAttributeEval	OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier.
class	PrincipalComponents	Performs a principal components analysis and transformation of the data.
class	Ranker	Ranker : Ranks attributes by their individual evaluations.
class	ReliefFAttributeEval	ReliefFAttributeEval : Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class.
class	SymmetricalUncertAttributeEval	SymmetricalUncertAttributeEval : Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
class	UnsupervisedAttributeEvaluator	Abstract unsupervised attribute evaluator.
class	UnsupervisedSubsetEvaluator	Abstract unsupervised attribute subset evaluator.
class	WrapperSubsetEval	WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.

Uses of OptionHandler in weka.classifiers

Classes in weka.classifiers that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractClassifier	Abstract classifier.
class	BVDecompose	Class for performing a Bias-Variance decomposition on any classifier using the method specified in: Ron Kohavi, David H.
class	BVDecomposeSegCVSub	This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1). The Kohavi and Wolpert definition of bias and variance is specified in (2). The Webb definition of bias and variance is specified in (3). Geoffrey I.
class	CheckClassifier	Class for examining the capabilities and finding problems with classifiers.
class	CheckSource	A simple class for checking the source generated from Classifiers implementing the weka.classifiers.Sourcable interface.
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.

Uses of OptionHandler in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement OptionHandler

Modifier and Type	Class	Description
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 an updateable multinomial Naive Bayes classifier.
class	NaiveBayesUpdateable	Class for a Naive Bayes classifier using estimator classes.

Uses of OptionHandler in weka.classifiers.bayes.net

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

Modifier and Type	Class	Description
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 OptionHandler in weka.classifiers.bayes.net.estimate

Classes in weka.classifiers.bayes.net.estimate that implement OptionHandler

Modifier and Type	Class	Description
class	BayesNetEstimator	BayesNetEstimator is the base class for estimating the conditional probability tables of a Bayes network once the structure has been learned.
class	BMAEstimator	BMAEstimator estimates conditional probability tables of a Bayes network using Bayes Model Averaging (BMA).
class	DiscreteEstimatorBayes	Symbolic probability estimator based on symbol counts and a prior.
class	DiscreteEstimatorFullBayes	Symbolic probability estimator based on symbol counts and a prior.
class	MultiNomialBMAEstimator	Multinomial BMA Estimator.
class	SimpleEstimator	SimpleEstimator is used for estimating the conditional probability tables of a Bayes network once the structure has been learned.

Uses of OptionHandler in weka.classifiers.bayes.net.search

Classes in weka.classifiers.bayes.net.search that implement OptionHandler

Modifier and Type	Class	Description
class	SearchAlgorithm	This is the base class for all search algorithms for learning Bayes networks.

Uses of OptionHandler in weka.classifiers.bayes.net.search.ci

Classes in weka.classifiers.bayes.net.search.ci that implement OptionHandler

Modifier and Type	Class	Description
class	CISearchAlgorithm	The CISearchAlgorithm class supports Bayes net structure search algorithms that are based on conditional independence test (as opposed to for example score based of cross validation based search algorithms).
class	ICSSearchAlgorithm	This Bayes Network learning algorithm uses conditional independence tests to find a skeleton, finds V-nodes and applies a set of rules to find the directions of the remaining arrows.

Uses of OptionHandler in weka.classifiers.bayes.net.search.fixed

Classes in weka.classifiers.bayes.net.search.fixed that implement OptionHandler

Modifier and Type	Class	Description
class	FromFile	The FromFile reads the structure of a Bayes net from a file in BIFF format.
class	NaiveBayes	The NaiveBayes class generates a fixed Bayes network structure with arrows from the class variable to each of the attribute variables.

Uses of OptionHandler in weka.classifiers.bayes.net.search.global

Classes in weka.classifiers.bayes.net.search.global that implement OptionHandler

Modifier and Type	Class	Description
class	GeneticSearch	This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
class	GlobalScoreSearchAlgorithm	This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.
class	HillClimber	This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
class	K2	This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables. For more information see: G.F.
class	RepeatedHillClimber	This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
class	SimulatedAnnealing	This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure. For more information see: R.R.
class	TabuSearch	This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
class	TAN	This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree. For more information see: N.

Uses of OptionHandler in weka.classifiers.bayes.net.search.local

Classes in weka.classifiers.bayes.net.search.local that implement OptionHandler

Modifier and Type	Class	Description
class	GeneticSearch	This Bayes Network learning algorithm uses genetic search for finding a well scoring Bayes network structure.
class	HillClimber	This Bayes Network learning algorithm uses a hill climbing algorithm adding, deleting and reversing arcs.
class	K2	This Bayes Network learning algorithm uses a hill climbing algorithm restricted by an order on the variables. For more information see: G.F.
class	LAGDHillClimber	This Bayes Network learning algorithm uses a Look Ahead Hill Climbing algorithm called LAGD Hill Climbing.
class	LocalScoreSearchAlgorithm	The ScoreBasedSearchAlgorithm class supports Bayes net structure search algorithms that are based on maximizing scores (as opposed to for example conditional independence based search algorithms).
class	RepeatedHillClimber	This Bayes Network learning algorithm repeatedly uses hill climbing starting with a randomly generated network structure and return the best structure of the various runs.
class	SimulatedAnnealing	This Bayes Network learning algorithm uses the general purpose search method of simulated annealing to find a well scoring network structure. For more information see: R.R.
class	TabuSearch	This Bayes Network learning algorithm uses tabu search for finding a well scoring Bayes network structure.
class	TAN	This Bayes Network learning algorithm determines the maximum weight spanning tree and returns a Naive Bayes network augmented with a tree. For more information see: N.

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

Classes in weka.classifiers.evaluation.output.prediction that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractOutput	A superclass for outputting the classifications of a classifier.
class	CSV	Outputs the predictions as CSV.
class	HTML	Outputs the predictions in HTML.
class	InMemory	* Stores the predictions in memory for programmatic retrieval. * Stores the instance, a prediction object and a map of attribute names with their associated values if an attribute was defined in a container per prediction. * The list of predictions can get retrieved using the getPredictions() method. * File output is disabled and buffer doesn't need to be supplied.
class	Null	Suppresses all output.
class	PlainText	Outputs the predictions in plain text.
class	XML	Outputs the predictions in XML. The following DTD is used: <!DOCTYPE predictions [ <!ELEMENT predictions (prediction*)> <!ATTLIST predictions version CDATA "3.5.8"> <!ATTLIST predictions name CDATA #REQUIRED> <!ELEMENT prediction ((actual_label,predicted_label,error,(prediction|distribution),attributes?)|(actual_value,predicted_value,error,attributes?))> <!ATTLIST prediction index CDATA #REQUIRED> <!ELEMENT actual_label ANY> <!ATTLIST actual_label index CDATA #REQUIRED> <!ELEMENT predicted_label ANY> <!ATTLIST predicted_label index CDATA #REQUIRED> <!ELEMENT error ANY> <!ELEMENT prediction ANY> <!ELEMENT distribution (class_label+)> <!ELEMENT class_label ANY> <!ATTLIST class_label index CDATA #REQUIRED> <!ATTLIST class_label predicted (yes|no) "no"> <!ELEMENT actual_value ANY> <!ELEMENT predicted_value ANY> <!ELEMENT attributes (attribute+)> <!ELEMENT attribute ANY> <!ATTLIST attribute index CDATA #REQUIRED> <!ATTLIST attribute name CDATA #REQUIRED> <!ATTLIST attribute type (numeric|date|nominal|string|relational) #REQUIRED> ] >

Uses of OptionHandler in weka.classifiers.functions

Classes in weka.classifiers.functions that implement OptionHandler

Modifier and Type	Class	Description
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 learn a multi-layer perceptron to classify instances.
class	SGD	Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss 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 OptionHandler in weka.classifiers.functions.supportVector

Classes in weka.classifiers.functions.supportVector that implement OptionHandler

Modifier and Type	Class	Description
class	CachedKernel	Base class for RBFKernel and PolyKernel that implements a simple LRU.
class	CheckKernel	Class for examining the capabilities and finding problems with kernels.
class	Kernel	Abstract kernel.
class	NormalizedPolyKernel	The normalized polynomial kernel. K(x,y) = <x,y>/sqrt(<x,x><y,y>) where <x,y> = PolyKernel(x,y)
class	PolyKernel	The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p
class	PrecomputedKernelMatrixKernel	This kernel is based on a static kernel matrix that is read from a file.
class	Puk	The Pearson VII function-based universal kernel. For more information see: B.
class	RBFKernel	The RBF kernel : K(x, y) = exp(-gamma*(x-y)^2) Valid options are:
class	RegOptimizer	Base class implementation for learning algorithm of SMOreg Valid options are:
class	RegSMO	Implementation of SMO for support vector regression as described in : A.J.
class	RegSMOImproved	Learn SVM for regression using SMO with Shevade, Keerthi, et al.
class	StringKernel	Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2]. For more information, see Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J.

Uses of OptionHandler in weka.classifiers.lazy

Classes in weka.classifiers.lazy that implement OptionHandler

Modifier and Type	Class	Description
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 OptionHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement OptionHandler

Modifier and Type	Class	Description
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	IterativeClassifierOptimizer	Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
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	RandomizableFilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
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.
class	WeightedInstancesHandlerWrapper	Generic wrapper around any classifier to enable weighted instances support. Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.

Uses of OptionHandler in weka.classifiers.misc

Classes in weka.classifiers.misc that implement OptionHandler

Modifier and Type	Class	Description
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.

Uses of OptionHandler in weka.classifiers.pmml.consumer

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

Modifier and Type	Class	Description
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 OptionHandler in weka.classifiers.rules

Classes in weka.classifiers.rules that implement OptionHandler

Modifier and Type	Class	Description
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 OptionHandler in weka.classifiers.trees

Classes in weka.classifiers.trees that implement OptionHandler

Modifier and Type	Class	Description
class	DecisionStump	Class for building and using a decision stump.
class	HoeffdingTree	A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time.
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 OptionHandler in weka.classifiers.trees.lmt

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

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

Uses of OptionHandler in weka.classifiers.trees.m5

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

Modifier and Type	Class	Description
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 OptionHandler in weka.clusterers

Classes in weka.clusterers that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractClusterer	Abstract clusterer.
class	AbstractDensityBasedClusterer	Abstract clustering model that produces (for each test instance) an estimate of the membership in each cluster (ie.
class	Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
class	CheckClusterer	Class for examining the capabilities and finding problems with clusterers.
class	Cobweb	Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	EM	Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
class	FarthestFirst	Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	FilteredClusterer	Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter.
class	HierarchicalClusterer	Hierarchical clustering class.
class	MakeDensityBasedClusterer	Class for wrapping a Clusterer to make it return a distribution and density.
class	RandomizableClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer	Abstract utility class for handling settings common to randomizable clusterers.
class	SimpleKMeans	Cluster data using the k means algorithm.
class	SingleClustererEnhancer	Meta-clusterer for enhancing a base clusterer.

Uses of OptionHandler in weka.core

Subinterfaces of OptionHandler in weka.core

Modifier and Type	Interface	Description
interface	DistanceFunction	Interface for any class that can compute and return distances between two instances.

Classes in weka.core that implement OptionHandler

Modifier and Type	Class	Description
class	AllJavadoc	Applies all known Javadoc-derived classes to a source file.
class	ChebyshevDistance	Implements the Chebyshev distance.
class	Check	Abstract general class for testing in Weka.
class	CheckGOE	Simple command line checking of classes that are editable in the GOE.
class	CheckOptionHandler	Simple command line checking of classes that implement OptionHandler.
class	CheckScheme	Abstract general class for testing schemes in Weka.
class	DictionaryBuilder	Class for building and maintaining a dictionary of terms.
class	EuclideanDistance	Implementing Euclidean distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	FilteredDistance	Applies the given filter before calling the given distance function.
class	FindWithCapabilities	Locates all classes with certain capabilities.
class	GlobalInfoJavadoc	Generates Javadoc comments from the class's globalInfo method.
class	Javadoc	Abstract superclass for classes that generate Javadoc comments and replace the content between certain comment tags.
class	ListOptions	Lists the options of an OptionHandler
class	ManhattanDistance	Implements the Manhattan distance (or Taxicab geometry).
class	MinkowskiDistance	Implementing Minkowski distance (or similarity) function. One object defines not one distance but the data model in which the distances between objects of that data model can be computed. Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low. For more information, see: Wikipedia.
class	NormalizableDistance	Represents the abstract ancestor for normalizable distance functions, like Euclidean or Manhattan distance.
class	OptionHandlerJavadoc	Generates Javadoc comments from the OptionHandler's options.
class	TechnicalInformationHandlerJavadoc	Generates Javadoc comments from the TechnicalInformationHandler's data.
class	TestInstances	Generates artificial datasets for testing.

Methods in weka.core that return OptionHandler

Modifier and Type	Method	Description
OptionHandler	CheckOptionHandler.getOptionHandler()	Get the OptionHandler used in the tests.
static OptionHandler	OptionHandler.makeCopy(OptionHandler toCopy)	Creates an instance of the class that the given option handler belongs to and sets the options for this new instance by taking the option settings from the given option handler.

Methods in weka.core with parameters of type OptionHandler

Modifier and Type	Method	Description
static OptionHandler	OptionHandler.makeCopy(OptionHandler toCopy)	Creates an instance of the class that the given option handler belongs to and sets the options for this new instance by taking the option settings from the given option handler.
void	CheckOptionHandler.setOptionHandler(OptionHandler value)	Set the OptionHandler to work on..

Uses of OptionHandler in weka.core.converters

Classes in weka.core.converters that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractFileSaver	Abstract class for Savers that save to a file Valid options are: -i input arff file The input filw in arff format.
class	ArffSaver	Writes to a destination in arff text format.
class	C45Saver	Writes to a destination that is in the format used by the C4.5 algorithm. Therefore it outputs a names and a data file.
class	CSVLoader	Reads a source that is in comma separated format (the default).
class	CSVSaver	Writes to a destination that is in CSV (comma-separated values) format.
class	DatabaseLoader	Reads Instances from a Database.
class	DatabaseSaver	Writes to a database (tested with MySQL, InstantDB, HSQLDB).
class	DictionarySaver	Writes a dictionary constructed from string attributes in incoming instances to a destination.
class	JSONSaver	Writes to a destination that is in JSON format. The data can be compressed with gzip, in order to save space. For more information, see JSON homepage: http://www.json.org/
class	LibSVMSaver	Writes to a destination that is in libsvm format. For more information about libsvm see: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
class	MatlabSaver	Writes Matlab ASCII files, in single or double precision format.
class	SerializedInstancesSaver	Serializes the instances to a file with extension bsi.
class	SVMLightSaver	Writes to a destination that is in svm light format. For more information about svm light see: http://svmlight.joachims.org/
class	TextDirectoryLoader	Loads all text files in a directory and uses the subdirectory names as class labels.
class	XRFFSaver	Writes to a destination that is in the XML version of the ARFF format.

Uses of OptionHandler in weka.core.neighboursearch

Classes in weka.core.neighboursearch that implement OptionHandler

Modifier and Type	Class	Description
class	BallTree	Class implementing the BallTree/Metric Tree algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	CoverTree	Class implementing the CoverTree datastructure. The class is very much a translation of the c source code made available by the authors. For more information and original source code see: Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest neighbor.
class	FilteredNeighbourSearch	Applies the given filter before calling the given neighbour search method.
class	KDTree	Class implementing the KDTree search algorithm for nearest neighbour search. The connection to dataset is only a reference.
class	LinearNNSearch	Class implementing the brute force search algorithm for nearest neighbour search.
class	NearestNeighbourSearch	Abstract class for nearest neighbour search.

Uses of OptionHandler in weka.core.neighboursearch.balltrees

Classes in weka.core.neighboursearch.balltrees that implement OptionHandler

Modifier and Type	Class	Description
class	BallSplitter	Abstract class for splitting a ball tree's BallNode.
class	BallTreeConstructor	Abstract class for constructing a BallTree .
class	BottomUpConstructor	The class that constructs a ball tree bottom up.
class	MedianDistanceFromArbitraryPoint	Class that splits a BallNode of a ball tree using Uhlmann's described method. For information see: Jeffrey K.
class	MedianOfWidestDimension	Class that splits a BallNode of a ball tree based on the median value of the widest dimension of the points in the ball.
class	MiddleOutConstructor	The class that builds a BallTree middle out. For more information see also: Andrew W.
class	PointsClosestToFurthestChildren	Implements the Moore's method to split a node of a ball tree. For more information please see section 2 of the 1st and 3.2.3 of the 2nd: Andrew W.
class	TopDownConstructor	The class implementing the TopDown construction method of ball trees.

Uses of OptionHandler in weka.core.neighboursearch.kdtrees

Classes in weka.core.neighboursearch.kdtrees that implement OptionHandler

Modifier and Type	Class	Description
class	KDTreeNodeSplitter	Class that splits up a KDTreeNode.
class	KMeansInpiredMethod	The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum. For more information see also: Ashraf Masood Kibriya (2007).
class	MedianOfWidestDimension	The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread. For more information see also: Jerome H.
class	MidPointOfWidestDimension	The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread. For more information see also: Andrew Moore (1991).
class	SlidingMidPointOfWidestSide	The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.

Uses of OptionHandler in weka.core.stemmers

Classes in weka.core.stemmers that implement OptionHandler

Modifier and Type	Class	Description
class	SnowballStemmer	A wrapper class for the Snowball stemmers.

Uses of OptionHandler in weka.core.stopwords

Classes in weka.core.stopwords that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractFileBasedStopwords	Ancestor for file-based stopword schemes.
class	AbstractStopwords	Ancestor for stopwords classes.
class	MultiStopwords	Applies the specified stopwords algorithms one after other. As soon as a word has been identified as stopword, the loop is exited.
class	Null	Dummy stopwords scheme, always returns false.
class	Rainbow	Stopwords list based on Rainbow: http://www.cs.cmu.edu/~mccallum/bow/rainbow/
class	RegExpFromFile	Uses the regular expressions stored in the file for determining whether a word is a stopword (ignored if pointing to a directory).
class	WordsFromFile	Uses the stopwords located in the specified file (ignored _if pointing to a directory).

Uses of OptionHandler in weka.core.tokenizers

Classes in weka.core.tokenizers that implement OptionHandler

Modifier and Type	Class	Description
class	AlphabeticTokenizer	Alphabetic string tokenizer, tokens are to be formed only from contiguous alphabetic sequences.
class	CharacterDelimitedTokenizer	Abstract superclass for tokenizers that take characters as delimiters.
class	CharacterNGramTokenizer	Splits a string into an n-gram with min and max grams.
class	NGramTokenizer	Splits a string into an n-gram with min and max grams.
class	Tokenizer	A superclass for all tokenizer algorithms.
class	WordTokenizer	A simple tokenizer that is using the java.util.StringTokenizer class to tokenize the strings.

Uses of OptionHandler in weka.datagenerators

Classes in weka.datagenerators that implement OptionHandler

Modifier and Type	Class	Description
class	ClassificationGenerator	Abstract class for data generators for classifiers.
class	ClusterDefinition	Ancestor to all ClusterDefinitions, i.e., subclasses that handle their own parameters that the cluster generator only passes on.
class	ClusterGenerator	Abstract class for cluster data generators.
class	DataGenerator	Abstract superclass for data generators that generate data for classifiers and clusterers.
class	RegressionGenerator	Abstract class for data generators for regression classifiers.

Uses of OptionHandler in weka.datagenerators.classifiers.classification

Classes in weka.datagenerators.classifiers.classification that implement OptionHandler

Modifier and Type	Class	Description
class	Agrawal	Generates a people database and is based on the paper by Agrawal et al.: R.
class	BayesNet	Generates random instances based on a Bayes network.
class	LED24	This generator produces data for a display with 7 LEDs.
class	RandomRBF	RandomRBF data is generated by first creating a random set of centers for each class.
class	RDG1	A data generator that produces data randomly by producing a decision list. The decision list consists of rules. Instances are generated randomly one by one.

Uses of OptionHandler in weka.datagenerators.classifiers.regression

Classes in weka.datagenerators.classifiers.regression that implement OptionHandler

Modifier and Type	Class	Description
class	Expression	A data generator for generating y according to a given expression out of randomly generated x. E.g., the mexican hat can be generated like this: sin(abs(a1)) / abs(a1) In addition to this function, the amplitude can be changed and gaussian noise can be added.
class	MexicanHat	A data generator for the simple 'Mexian Hat' function: y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added.

Uses of OptionHandler in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement OptionHandler

Modifier and Type	Class	Description
class	BIRCHCluster	Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center.
class	SubspaceCluster	A data generator that produces data points in hyperrectangular subspace clusters.
class	SubspaceClusterDefinition	A single cluster for the SubspaceCluster data generator.

Uses of OptionHandler in weka.estimators

Classes in weka.estimators that implement OptionHandler

Modifier and Type	Class	Description
class	CheckEstimator	Class for examining the capabilities and finding problems with estimators.
class	DiscreteEstimator	Simple symbolic probability estimator based on symbol counts.
class	Estimator	Abstract class for all estimators.
class	KernelEstimator	Simple kernel density estimator.
class	MahalanobisEstimator	Simple probability estimator that places a single normal distribution over the observed values.
class	NormalEstimator	Simple probability estimator that places a single normal distribution over the observed values.
class	PoissonEstimator	Simple probability estimator that places a single Poisson distribution over the observed values.
class	UnivariateMixtureEstimator	Simple weighted mixture density estimator.

Uses of OptionHandler in weka.experiment

Classes in weka.experiment that implement OptionHandler

Modifier and Type	Class	Description
class	AveragingResultProducer	Takes the results from a ResultProducer and submits the average to the result listener.
class	ClassifierSplitEvaluator	A SplitEvaluator that produces results for a classification scheme on a nominal class attribute.
class	CostSensitiveClassifierSplitEvaluator	SplitEvaluator that produces results for a classification scheme on a nominal class attribute, including weighted misclassification costs.
class	CrossValidationResultProducer	Generates for each run, carries out an n-fold cross-validation, using the set SplitEvaluator to generate some results.
class	CrossValidationSplitResultProducer	Carries out one split of a repeated k-fold cross-validation, using the set SplitEvaluator to generate some results.
class	CSVResultListener	Takes results from a result producer and assembles them into comma separated value form.
class	DatabaseResultProducer	Examines a database and extracts out the results produced by the specified ResultProducer and submits them to the specified ResultListener.
class	DensityBasedClustererSplitEvaluator	A SplitEvaluator that produces results for a density based clusterer.
class	Experiment	Holds all the necessary configuration information for a standard type experiment.
class	ExplicitTestsetResultProducer	Loads the external test set and calls the appropriate SplitEvaluator to generate some results. The filename of the test set is constructed as follows: <dir> + / + <prefix> + <relation-name> + <suffix> The relation-name can be modified by using the regular expression to replace the matching sub-string with a specified replacement string.
class	InstanceQuery	Convert the results of a database query into instances.
class	InstancesResultListener	Outputs the received results in arff format to a Writer.
class	LearningRateResultProducer	Tells a sub-ResultProducer to reproduce the current run for varying sized subsamples of the dataset.
class	PairedCorrectedTTester	Behaves the same as PairedTTester, only it uses the corrected resampled t-test statistic.
class	PairedTTester	Calculates T-Test statistics on data stored in a set of instances.
class	RandomSplitResultProducer	Generates a single train/test split and calls the appropriate SplitEvaluator to generate some results.
class	RegressionSplitEvaluator	A SplitEvaluator that produces results for a classification scheme on a numeric class attribute.
class	RemoteExperiment	Holds all the necessary configuration information for a distributed experiment.
class	ResultMatrix	This matrix is a container for the datasets and classifier setups and their statistics.
class	ResultMatrixCSV	Generates the matrix in CSV ('comma-separated values') format.
class	ResultMatrixGnuPlot	Generates output for a data and script file for GnuPlot.
class	ResultMatrixHTML	Generates the matrix output as HTML.
class	ResultMatrixLatex	Generates the matrix output in LaTeX-syntax.
class	ResultMatrixPlainText	Generates the output as plain text (for fixed width fonts).
class	ResultMatrixSignificance	Only outputs the significance indicators.

Uses of OptionHandler in weka.filters

Classes in weka.filters that implement OptionHandler

Modifier and Type	Class	Description
class	AllFilter	A simple instance filter that passes all instances directly through.
class	CheckSource	A simple class for checking the source generated from Filters implementing the weka.filters.Sourcable interface.
class	Filter	An abstract class for instance filters: objects that take instances as input, carry out some transformation on the instance and then output the instance.
class	MultiFilter	Applies several filters successively.
class	RenameRelation	A simple filter that allows the relation name of a set of instances to be altered in various ways.
class	SimpleBatchFilter	This filter is a superclass for simple batch filters.
class	SimpleFilter	This filter contains common behavior of the SimpleBatchFilter and the SimpleStreamFilter.
class	SimpleStreamFilter	This filter is a superclass for simple stream filters.

Uses of OptionHandler in weka.filters.supervised.attribute

Classes in weka.filters.supervised.attribute that implement OptionHandler

Modifier and Type	Class	Description
class	AddClassification	A filter for adding the classification, the class distribution and an error flag to a dataset with a classifier.
class	AttributeSelection	A supervised attribute filter that can be used to select attributes.
class	ClassConditionalProbabilities	Converts the values of nominal and/or numeric attributes into class conditional probabilities.
class	ClassOrder	Changes the order of the classes so that the class values are no longer of in the order specified in the header.
class	Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	MergeNominalValues	Merges values of all nominal attributes among the specified attributes, excluding the class attribute, using the CHAID method, but without considering re-splitting of merged subsets.
class	NominalToBinary	Converts all nominal attributes into binary numeric attributes.
class	PartitionMembership	* A filter that uses a PartitionGenerator to generate partition membership values; filtered instances are composed of these values plus the class attribute (if set in the input data) and rendered as sparse instances.

Uses of OptionHandler in weka.filters.supervised.instance

Classes in weka.filters.supervised.instance that implement OptionHandler

Modifier and Type	Class	Description
class	ClassBalancer	Reweights the instances in the data so that each class has the same total weight.
class	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement. The original dataset must fit entirely in memory.
class	SpreadSubsample	Produces a random subsample of a dataset.
class	StratifiedRemoveFolds	This filter takes a dataset and outputs a specified fold for cross validation.

Uses of OptionHandler in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractTimeSeries	An abstract instance filter that assumes instances form time-series data and performs some merging of attribute values in the current instance with attribute attribute values of some previous (or future) instance.
class	Add	An instance filter that adds a new attribute to the dataset.
class	AddCluster	A filter that adds a new nominal attribute representing the cluster assigned to each instance by the specified clustering algorithm. Either the clustering algorithm gets built with the first batch of data or one specifies are serialized clusterer model file to use instead.
class	AddExpression	An instance filter that creates a new attribute by applying a mathematical expression to existing attributes.
class	AddID	An instance filter that adds an ID attribute to the dataset.
class	AddNoise	An instance filter that changes a percentage of a given attribute's values.
class	AddUserFields	A filter that adds new attributes with user specified type and constant value.
class	AddValues	Adds the labels from the given list to an attribute if they are missing.
class	CartesianProduct	A filter for performing the Cartesian product of a set of nominal attributes.
class	Center	Centers all numeric attributes in the given dataset to have zero mean (apart from the class attribute, if set).
class	ChangeDateFormat	Changes the date format used by a date attribute.
class	ClassAssigner	Filter that can set and unset the class index.
class	ClusterMembership	A filter that uses a density-based clusterer to generate cluster membership values; filtered instances are composed of these values plus the class attribute (if set in the input data).
class	Copy	An instance filter that copies a range of attributes in the dataset.
class	DateToNumeric	A filter for turning date attributes into numeric ones.
class	Discretize	An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
class	FirstOrder	This instance filter takes a range of N numeric attributes and replaces them with N-1 numeric attributes, the values of which are the difference between consecutive attribute values from the original instance.
class	FixedDictionaryStringToWordVector	Converts String attributes into a set of attributes representing word occurrence (depending on the tokenizer) information from the text contained in the strings.
class	InterquartileRange	A filter for detecting outliers and extreme values based on interquartile ranges.
class	KernelFilter	Converts the given set of data into a kernel matrix.
class	MakeIndicator	A filter that creates a new dataset with a Boolean attribute replacing a nominal attribute.
class	MathExpression	Modify numeric attributes according to a given mathematical expression.
class	MergeInfrequentNominalValues	Merges all values of the specified nominal attributes that are insufficiently frequent.
class	MergeManyValues	Merges many values of a nominal attribute into one value.
class	MergeTwoValues	Merges two values of a nominal attribute into one value.
class	NominalToBinary	Converts all nominal attributes into binary numeric attributes.
class	NominalToString	Converts a nominal attribute (i.e.
class	Normalize	Normalizes all numeric values in the given dataset (apart from the class attribute, if set).
class	NumericCleaner	A filter that 'cleanses' the numeric data from values that are too small, too big or very close to a certain value, and sets these values to a pre-defined default.
class	NumericToBinary	Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero.
class	NumericToDate	A filter for turning numeric attributes into date attributes.
class	NumericToNominal	A filter for turning numeric attributes into nominal ones.
class	NumericTransform	Transforms numeric attributes using a given transformation method.
class	Obfuscate	A simple instance filter that renames the relation, all attribute names and all nominal attribute values.
class	OrdinalToNumeric	An attribute filter that converts ordinal nominal attributes into numeric ones Valid options are:
class	PartitionedMultiFilter	A filter that applies filters on subsets of attributes and assembles the output into a new dataset.
class	PKIDiscretize	Discretizes numeric attributes using equal frequency binning and forces the number of bins to be equal to the square root of the number of values of the numeric attribute. For more information, see: Ying Yang, Geoffrey I.
class	PotentialClassIgnorer	This filter should be extended by other unsupervised attribute filters to allow processing of the class attribute if that's required.
class	PrincipalComponents	Performs a principal components analysis and transformation of the data. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data -- default 0.95 (95%). Based on code of the attribute selection scheme 'PrincipalComponents' by Mark Hall and Gabi Schmidberger.
class	RandomProjection	Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
class	RandomSubset	Chooses a random subset of non-class attributes, either an absolute number or a percentage.
class	Remove	An filter that removes a range of attributes from the dataset.
class	RemoveByName	Removes attributes based on a regular expression matched against their names.
class	RemoveType	Removes attributes of a given type.
class	RemoveUseless	This filter removes attributes that do not vary at all or that vary too much.
class	RenameAttribute	This filter is used for renaming attributes. Regular expressions can be used in the matching and replacing. See Javadoc of java.util.regex.Pattern class for more information: http://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html
class	RenameNominalValues	Renames the values of nominal attributes.
class	Reorder	A filter that generates output with a new order of the attributes.
class	ReplaceMissingValues	Replaces all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data.
class	ReplaceMissingWithUserConstant	Replaces all missing values for nominal, string, numeric and date attributes in the dataset with user-supplied constant values.
class	ReplaceWithMissingValue	A filter that can be used to introduce missing values in a dataset.
class	SortLabels	A simple filter for sorting the labels of nominal attributes.
class	Standardize	Standardizes all numeric attributes in the given dataset to have zero mean and unit variance (apart from the class attribute, if set).
class	StringToNominal	Converts a range of string attributes (unspecified number of values) to nominal (set number of values).
class	StringToWordVector	Converts string attributes into a set of numeric attributes representing word occurrence information from the text contained in the strings.
class	SwapValues	Swaps two values of a nominal attribute.
class	TimeSeriesDelta	An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the difference between the current value and the equivalent attribute attribute value of some previous (or future) instance.
class	TimeSeriesTranslate	An instance filter that assumes instances form time-series data and replaces attribute values in the current instance with the equivalent attribute values of some previous (or future) instance.
class	Transpose	Transposes the data: instances become attributes and attributes become instances.

Uses of OptionHandler in weka.filters.unsupervised.instance

Classes in weka.filters.unsupervised.instance that implement OptionHandler

Modifier and Type	Class	Description
class	NonSparseToSparse	An instance filter that converts all incoming instances into sparse format.
class	Randomize	Randomly shuffles the order of instances passed through it.
class	RemoveDuplicates	Removes all duplicate instances from the first batch of data it receives.
class	RemoveFolds	This filter takes a dataset and outputs a specified fold for cross validation.
class	RemoveFrequentValues	Determines which values (frequent or infrequent ones) of an (nominal) attribute are retained and filters the instances accordingly.
class	RemoveMisclassified	A filter that removes instances which are incorrectly classified.
class	RemovePercentage	A filter that removes a given percentage of a dataset.
class	RemoveRange	A filter that removes a given range of instances of a dataset.
class	RemoveWithValues	Filters instances according to the value of an attribute.
class	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement.
class	ReservoirSample	Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.
class	SparseToNonSparse	An instance filter that converts all incoming sparse instances into non-sparse format.
class	SubsetByExpression	Filters instances according to a user-specified expression. Examples: - extracting only mammals and birds from the 'zoo' UCI dataset: (CLASS is 'mammal') or (CLASS is 'bird') - extracting only animals with at least 2 legs from the 'zoo' UCI dataset: (ATT14 >= 2) - extracting only instances with non-missing 'wage-increase-second-year' from the 'labor' UCI dataset: not ismissing(ATT3)

Uses of OptionHandler in weka.gui

Classes in weka.gui that implement OptionHandler

Modifier and Type	Class	Description
class	Main	Menu-based GUI for Weka, replacement for the GUIChooser.

Uses of OptionHandler in weka.gui.explorer

Classes in weka.gui.explorer that implement OptionHandler

Modifier and Type	Class	Description
class	AbstractPlotInstances	Abstract superclass for generating plottable instances.
class	ClassifierErrorsPlotInstances	A class for generating plottable visualization errors.
class	ClustererAssignmentsPlotInstances	A class for generating plottable cluster assignments.

Uses of OptionHandler in weka.gui.scripting

Classes in weka.gui.scripting that implement OptionHandler

Modifier and Type	Class	Description
class	GroovyScript	Represents a Groovy script.
class	JythonScript	Represents a Jython script.
class	Script	A simple helper class for loading, saving scripts.