Node Connectivity
There are 4972 nodes that can be used as predessesor
for a node with an input port of type 
Generic Port.
RandomForest (3.6) (legacy)
Class for constructing a forest of random trees. For more information see: Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
RandomTree (3.6) (legacy)
Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option to allow estimation of […]
SimpleCart (3.6) (legacy)
Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split […]
UserClassifier (3.6) (legacy)
Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of […]
ConjunctiveRule (3.6) (legacy)
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed […]
DTNB (3.6) (legacy)
Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the […]
DecisionTable (3.6) (legacy)
Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables. In: 8th […]
JRip (3.6) (legacy)
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. Cohen […]
M5Rules (3.6) (legacy)
Generates a decision list for regression problems using separate-and-conquer. In each iteration it builds a model tree using M5 and makes the "best" leaf […]
NNge (3.6) (legacy)
Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). For more […]