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This category contains 32 nodes.

OrdinalClassClassifier (3.6) (legacy) 

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: […]

RacedIncrementalLogitBoost (3.6) (legacy) 

Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, […]

RandomCommittee (3.6) (legacy) 

Class for building an ensemble of randomizable base classifiers. Each base classifiers is built using a different random number seed (but based one the same […]

RandomSubSpace (3.6) (legacy) 

This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows […]

RegressionByDiscretization (3.6) (legacy) 

A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the […]

RotationForest (3.6) (legacy) 

Class for construction a Rotation Forest. Can do classification and regression depending on the base learner. For more information, see Juan J. […]

Stacking (3.6) (legacy) 

Combines several classifiers using the stacking method. Can do classification or regression. For more information, see David H. Wolpert (1992). Stacked […]

StackingC (3.6) (legacy) 

Implements StackingC (more efficient version of stacking). For more information, see A.K. Seewald: How to Make Stacking Better and Faster While Also […]

ThresholdSelector (3.6) (legacy) 

A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance […]

Vote (3.6) (legacy) 

Class for combining classifiers. Different combinations of probability estimates for classification are available. For more information see: Ludmila I. […]