Node Connectivity
There are 2898 nodes that can be used as successor
for a node with an output port of type 
Table.
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 […]
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 […]
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 […]
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 […]