# Package weka.classifiers.meta

- Class Summary
  Class | Description
  --- | ---
  AdaBoostM1 | Class for boosting a nominal class classifier using the Adaboost M1 method.
  AdditiveRegression | Meta classifier that enhances the performance of a regression base classifier.
  AttributeSelectedClassifier | Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
  Bagging | Class for bagging a classifier to reduce variance.
  ClassificationViaRegression | Class for doing classification using regression methods.
  CostSensitiveClassifier | A metaclassifier that makes its base classifier cost-sensitive.
  CVParameterSelection | Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
  FilteredClassifier | Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
  IterativeClassifierOptimizer | Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation.
  LogitBoost | Class for performing additive logistic regression.
  MultiClassClassifier | A metaclassifier for handling multi-class datasets with 2-class classifiers.
  MultiClassClassifierUpdateable | A metaclassifier for handling multi-class datasets with 2-class classifiers.
  MultiScheme | Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
  RandomCommittee | Class for building an ensemble of randomizable base classifiers.
  RandomizableFilteredClassifier | Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
  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.
  RegressionByDiscretization | A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
  Stacking | Combines several classifiers using the stacking method.
  Vote | Class for combining classifiers.