Node Connectivity
There are 5640 nodes that can be used as predessesor
for a node with an input port of type 
Generic Port.
Vote (3.6) (legacy)
Class for combining classifiers. Different combinations of probability estimates for classification are available. For more information see: Ludmila I. […]
HyperPipes (3.6) (legacy)
Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the […]
SerializedClassifier (3.6) (legacy)
A wrapper around a serialized classifier model. This classifier loads a serialized models and uses it to make predictions. Warning: since the serialized […]
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 […]