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.
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). […]
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 […]