Node Connectivity
There are 3042 nodes that can be used as successor
for a node with an output port of type 
Table.
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 […]
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. […]
Decorate (3.6) (legacy)
DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive […]
END (3.6) (legacy)
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) (legacy)
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) (legacy)
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) (legacy)
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) (legacy)
Class for performing additive logistic regression. This class performs classification using a regression scheme as the base learner, and can handle […]
MetaCost (3.6) (legacy)
This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making […]