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J48 (Weka)

DeprecatedKNIME WEKA nodes version 2.7.3.v202012011124 by KNIME AG, Zurich, Switzerland

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 Kaufmann Publishers, San Mateo, CA. Use this node to build a DecisionTree model that can later be used by the DecisionTreePredictor node. The view provides a visual model of the DecisionTree as well as some statistical output (number of nodes and leaves).


Whether to use binary splits on nominal attributes when building the trees.
The confidence factor used for pruning (smaller values incur more pruning).
The value of this option is ignored in Knime.
The minimum number of instances per leaf.
Determines the amount of data used for reduced-error pruning. One fold is used for pruning, the rest for growing the tree.
Whether reduced-error pruning is used instead of C.4.5 pruning.
Whether to save the training data for visualization.
The seed used for randomizing the data when reduced-error pruning is used.
Whether to consider the subtree raising operation when pruning.
Whether pruning is performed. (Due to an issue in the weka code, once this option is set to true, the reducedErrorPruning can not be changed anymore)
Whether counts at leaves are smoothed based on Laplace.

Input Ports

Training data

Output Ports

PMML Decision Tree Model


Decision Tree View
The Decision Tree view provides a tree view of KNIME in addition to the tree view of weka and some summary information.


To use this node in KNIME, install KNIME Weka Data Mining Integration from the following update site:


A zipped version of the software site can be downloaded here.

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