meta
This category contains 32 nodes.
AdaBoostM1 (3.6) (legacy)
Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves […]
AdditiveRegression (3.6) (legacy)
Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the […]
AttributeSelectedClassifier (3.6) (legacy)
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
Bagging (3.6) (legacy)
Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more information, see Leo […]
ClassificationViaClustering (3.6) (legacy)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user […]
ClassificationViaRegression (3.6) (legacy)
Class for doing classification using regression methods. Class is binarized and one regression model is built for each class value. For more information, […]
CostSensitiveClassifier (3.6) (legacy)
A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances […]
CVParameterSelection (3.6) (legacy)
Class for performing parameter selection by cross-validation for any classifier. For more information, see: R. Kohavi (1995). Wrappers for Performance […]
Dagging (3.6) (legacy)
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. […]