meta
This category contains 32 nodes.
OrdinalClassClassifier (3.6)
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)
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)
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)
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)
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)
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)
Combines several classifiers using the stacking method. Can do classification or regression. For more information, see David H. Wolpert (1992). Stacked […]
StackingC (3.6)
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)
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)
Class for combining classifiers. Different combinations of probability estimates for classification are available. For more information see: Ludmila I. […]