Uses of Class
weka.attributeSelection.ASEvaluation

Packages that use ASEvaluation

Package	Description
weka.attributeSelection
weka.classifiers.meta
weka.filters.supervised.attribute
weka.knowledgeflow.steps

Uses of ASEvaluation in weka.attributeSelection

Subclasses of ASEvaluation in weka.attributeSelection

Modifier and Type	Class	Description
class	AttributeSetEvaluator	Abstract attribute set evaluator.
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	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	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	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.

Methods in weka.attributeSelection that return ASEvaluation

Modifier and Type	Method	Description
static ASEvaluation	ASEvaluation.forName(String evaluatorName, String[] options)	Creates a new instance of an attribute/subset evaluator given it's class name and (optional) arguments to pass to it's setOptions method.
ASEvaluation	CheckAttributeSelection.getEvaluator()	Get the current evaluator
static ASEvaluation[]	ASEvaluation.makeCopies(ASEvaluation model, int num)	Creates copies of the current evaluator.

Methods in weka.attributeSelection with parameters of type ASEvaluation

Modifier and Type	Method	Description
static ASEvaluation[]	ASEvaluation.makeCopies(ASEvaluation model, int num)	Creates copies of the current evaluator.
static void	ASEvaluation.runEvaluator(ASEvaluation evaluator, String[] options)	runs the evaluator with the given commandline options
abstract int[]	ASSearch.search(ASEvaluation ASEvaluator, Instances data)	Searches the attribute subset/ranking space.
int[]	BestFirst.search(ASEvaluation ASEval, Instances data)	Searches the attribute subset space by best first search
int[]	GreedyStepwise.search(ASEvaluation ASEval, Instances data)	Searches the attribute subset space by forward selection.
int[]	Ranker.search(ASEvaluation ASEval, Instances data)	Kind of a dummy search algorithm.
static String	AttributeSelection.SelectAttributes(ASEvaluation ASEvaluator, String[] options)	Perform attribute selection with a particular evaluator and a set of options specifying search method and input file etc.
static String	AttributeSelection.SelectAttributes(ASEvaluation ASEvaluator, String[] options, Instances train)	Perform attribute selection with a particular evaluator and a set of options specifying search method and options for the search method and evaluator.
void	AttributeSelection.setEvaluator(ASEvaluation evaluator)	set the attribute/subset evaluator
void	CheckAttributeSelection.setEvaluator(ASEvaluation value)	Set the evaluator to test.
void	AttributeSelection.updateStatsForModelCVSplit(Instances split, ASEvaluation evaluator, ASSearch search, int[] attributeSet, boolean doRank)	Update the attribute selection stats for a cross-validation fold of the data.

Uses of ASEvaluation in weka.classifiers.meta

Methods in weka.classifiers.meta that return ASEvaluation

Modifier and Type	Method	Description
ASEvaluation	AttributeSelectedClassifier.getEvaluator()	Gets the attribute evaluator used

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

Modifier and Type	Method	Description
void	AttributeSelectedClassifier.setEvaluator(ASEvaluation evaluator)	Sets the attribute evaluator

Uses of ASEvaluation in weka.filters.supervised.attribute

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

Modifier and Type	Method	Description
ASEvaluation	AttributeSelection.getEvaluator()	Get the name of the attribute/subset evaluator

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

Modifier and Type	Method	Description
void	AttributeSelection.setEvaluator(ASEvaluation evaluator)	set attribute/subset evaluator

Uses of ASEvaluation in weka.knowledgeflow.steps

Methods in weka.knowledgeflow.steps that return ASEvaluation

Modifier and Type	Method	Description
ASEvaluation	ASEvaluator.getEvaluator()	Get the evaluator wrapped by this step

Methods in weka.knowledgeflow.steps with parameters of type ASEvaluation

Modifier and Type	Method	Description
void	ASEvaluator.setEvaluator(ASEvaluation eval)	Set the evaluator to wrap (just calls setWrappedAlgorithm)