Node Connectivity
There are 4972 nodes that can be used as predessesor
for a node with an input port of type 
Generic Port.
DecisionStump (3.6) (legacy)
Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or […]
FT (3.6) (legacy)
Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves. […]
Id3 (3.6) (legacy)
Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves […]
J48 (3.6) (legacy)
Class for generating a pruned or unpruned C4.5 decision tree. For more information, see Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan […]
J48graft (3.6) (legacy)
Class for generating a grafted (pruned or unpruned) C4.5 decision tree. For more information, see Geoff Webb: Decision Tree Grafting From the […]
LADTree (3.6) (legacy)
Class for generating a multi-class alternating decision tree using the LogitBoost strategy. For more info, see Geoffrey Holmes, Bernhard Pfahringer, […]
LMT (3.6) (legacy)
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. The algorithm can deal with […]
M5P (3.6) (legacy)
M5Base. Implements base routines for generating M5 Model trees and rules The original algorithm M5 was invented by R. Quinlan and Yong Wang made […]
NBTree (3.6) (legacy)
Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of […]
REPTree (3.6) (legacy)
Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). […]