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## Package weka.classifiers.meta

Class Summary
AdaBoostM1 | Class for boosting a classifier using Freund & Schapire's Adaboost M1 method.
AdditiveRegression | Meta classifier that enhances the performance of a regression base classifier.
AttributeSelectedClassifier | Class for running an arbitrary classifier on data that has been reduced through attribute selection.
Bagging | Class for bagging a classifier.
ClassificationViaRegression | Class for doing classification using regression methods.
CostSensitiveClassifier | This metaclassifier makes its base classifier cost-sensitive.
CVParameterSelection | Class for performing parameter selection by cross-validation for any classifier.
Decorate | DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
FilteredClassifier | Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
Grading | Implements Grading.
LogitBoost | Class for performing additive logistic regression..
MetaCost | This metaclassifier makes its base classifier cost-sensitive using the method specified in
MultiBoostAB | Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
MultiClassClassifier | Class for handling multi-class datasets with 2-class distribution classifiers.
MultiScheme | Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
OrdinalClassClassifier | Meta classifier for transforming an ordinal class problem to a series of binary class problems.
RacedIncrementalLogitBoost | Classifier for incremental learning of large datasets by way of racing logit-boosted committees.
RandomCommittee | Class for creating a committee of random classifiers.
RegressionByDiscretization | Class for a regression scheme that employs any distribution classifier on a copy of the data that has the class attribute (equal-width) discretized.
Stacking | Implements stacking.
StackingC | Implements StackingC (more efficient version of stacking).
ThresholdSelector | Class for selecting a threshold on a probability output by a distribution classifier.
Vote | Class for combining classifiers using unweighted average of probability estimates (classification) or numeric predictions (regression).

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