# Package weka.attributeSelection

- Interface Summary
  Interface | Description
  --- | ---
  AttributeEvaluator | Interface for classes that evaluate attributes individually.
  AttributeTransformer | Abstract attribute transformer.
  ErrorBasedMeritEvaluator | Interface for evaluators that calculate the "merit" of attributes/subsets as the error of a learning scheme
  RankedOutputSearch | Interface for search methods capable of producing a ranked list of attributes.
  StartSetHandler | Interface for search methods capable of doing something sensible given a starting set of attributes.
  SubsetEvaluator | Interface for attribute subset evaluators.

- Class Summary
  Class | Description
  --- | ---
  ASEvaluation | Abstract attribute selection evaluation class
  ASSearch | Abstract attribute selection search class.
  AttributeSelection | Attribute selection class.
  AttributeSetEvaluator | Abstract attribute set evaluator.
  BestFirst | BestFirst: Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility.
  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.
  CheckAttributeSelection | Class for examining the capabilities and finding problems with attribute selection schemes.
  ClassifierAttributeEval | ClassifierAttributeEval : Evaluates the worth of an attribute by using a user-specified classifier. Valid options are:
  ClassifierSubsetEval | Classifier subset evaluator: Evaluates attribute subsets on training data or a seperate hold out testing set.
  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.
  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). Valid options are:
  GreedyStepwise | GreedyStepwise : Performs a greedy forward or backward search through the space of attribute subsets.
  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.
  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). Valid options are:
  OneRAttributeEval | OneRAttributeEval : Evaluates the worth of an attribute by using the OneR classifier. Valid options are:
  PrincipalComponents | Performs a principal components analysis and transformation of the data.
  Ranker | Ranker : Ranks attributes by their individual evaluations.
  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.
  SymmetricalUncertAttributeEval | SymmetricalUncertAttributeEval : Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
  UnsupervisedAttributeEvaluator | Abstract unsupervised attribute evaluator.
  UnsupervisedSubsetEvaluator | Abstract unsupervised attribute subset evaluator.
  WrapperSubsetEval | WrapperSubsetEval: Evaluates attribute sets by using a learning scheme.