# 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 or a percentage split evaluation.
  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.
  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.