meta
This category contains 32 nodes.
Decorate (3.6)
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive […]
END (3.6)
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)
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)
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)
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)
Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle […]
MetaCost (3.6)
This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making […]
MultiBoostAB (3.6)
Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming […]
MultiClassClassifier (3.6)
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)
Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is […]