Uses of Interface
weka.core.ModelOutputHandler

Packages that use ModelOutputHandler

Package	Description
weka.classifiers.meta

Uses of ModelOutputHandler in weka.classifiers.meta

Classes in weka.classifiers.meta that implement ModelOutputHandler

Modifier and Type	Class	Description
class	AbstainingCascade	The specified classifiers represent a cascade: if the first one abstains, the second is used (and so on), otherwise the prediction is returned. If all classifiers prior to the last one abstained then the prediction of the last one is returned.
class	ConsensusOrVote	If the required minimum number of classifiers of the ensemble agree on a label, then this label is predicted.
class	SuppressModelOutput	Meta-classifier that enables the user to suppress the model output. Useful for ensembles, since their output can be extremely long.
class	ThresholdedBinaryClassification	Meta classifier for binary classification problems that allows to specify a minimum probability threshold for one of the labels.
class	Veto	If the specified label is predicted by the required minimum number of classifiers of the ensemble, then this label is predicted.
class	VotedImbalance	Generates an ensemble using the following approach: - do x times: * create new dataset, resampled with specified bias * build base classifier with it If no classifier gets built at all, use ZeroR as backup model, built on the full dataset. At prediction time, the Vote meta-classifier (using the pre-built classifiers) is used to determining the class probabilities or regression value. Instead of just using a fixed number of resampled models, you can also specify thresholds (= probability that the minority class does not meet) with associated number of resampled models to use.