Node Connectivity
There are 99 nodes that can be used as predessesor
for a node with an input port of type 
Weka 3.6 Classifier.
VFI (3.6) (legacy)
Classification by voting feature intervals. Intervals are constucted around each class for each attribute (basically discretization). Class counts are […]
ADTree (3.6) (legacy)
Class for generating an alternating decision tree. The basic algorithm is based on: Freund, Y., Mason, L.: The alternating decision tree learning […]
BFTree (3.6) (legacy)
Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the […]
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 […]