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Package

weka.classifiers.trees

Synopsis

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.

Options

The table below describes the options available for J48.

Option

Description

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

If set to true, classifier may output additional info to the console.

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.

useLaplace

Whether counts at leaves are smoothed based on Laplace.

Capabilities

The table below describes the capabilites of J48.

Capability

Supported

Class

Nominal class, Missing class values, Binary class

Attributes

Missing values, Numeric attributes, Unary attributes, Date attributes, Binary attributes, Empty nominal attributes, Nominal attributes

Min # of instances

0

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