J48 (Weka)

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).

Options

binarySplits

Whether to use binary splits on nominal attributes when building the trees.

confidenceFactor

The confidence factor used for pruning (smaller values incur more pruning).

debug

The value of this option is ignored in Knime.

minNumObj

The minimum number of instances per leaf.

numFolds

Determines the amount of data used for reduced-error pruning. One fold is used for pruning, the rest for growing the tree.

reducedErrorPruning

Whether reduced-error pruning is used instead of C.4.5 pruning.

saveInstanceData

Whether to save the training data for visualization.

seed

The seed used for randomizing the data when reduced-error pruning is used.

subtreeRaising

Whether to consider the subtree raising operation when pruning.

unpruned

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)

useLaplace

Whether counts at leaves are smoothed based on Laplace.

Views

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.

Workflows

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Links

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Developers