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. […]