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.

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.

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