This category contains 34 nodes.

AdaBoostM1 (3.7) 

Class for boosting a nominal class classifier using the Adaboost M1 method

AdditiveRegression (3.7) 

Meta classifier that enhances the performance of a regression base classifier

AttributeSelectedClassifier (3.7) 

Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier

Bagging (3.7) 

Class for bagging a classifier to reduce variance

ClassificationViaClustering (3.7) 

A simple meta-classifier that uses a clusterer for classification

ClassificationViaRegression (3.7) 

Class for doing classification using regression methods

CostSensitiveClassifier (3.7) 

A metaclassifier that makes its base classifier cost-sensitive

CVParameterSelection (3.7) 

Class for performing parameter selection by cross-validation for any classifier. For more information, see: R

Dagging (3.7) 

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