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This category contains 32 nodes.

Decorate (3.6) (legacy) 

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive […]

END (3.6) (legacy) 

A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, […]

FilteredClassifier (3.6) (legacy) 

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is […]

Grading (3.6) (legacy) 

Implements Grading. The base classifiers are "graded". For more information, see A.K. Seewald, J. Fuernkranz: An Evaluation of Grading Classifiers. In: […]

GridSearch (3.6) (legacy) 

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, […]

LogitBoost (3.6) (legacy) 

Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle […]

MetaCost (3.6) (legacy) 

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making […]

MultiBoostAB (3.6) (legacy) 

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming […]

MultiClassClassifier (3.6) (legacy) 

A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for […]

MultiScheme (3.6) (legacy) 

Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is […]