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Package

weka.classifiers.trees

Synopsis

Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).

Options

The table below describes the options available for REPTree.

Option

Description

debug

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

maxDepth

The maximum tree depth (-1 for no restriction).

minNum

The minimum total weight of the instances in a leaf.

minVarianceProp

The minimum proportion of the variance on all the data that needs to be present at a node in order for splitting to be performed in regression trees.

noPruning

Whether pruning is performed.

numFolds

Determines the amount of data used for pruning. One fold is used for pruning, the rest for growing the rules.

seed

The seed used for randomizing the data.

Capabilities

The table below describes the capabilites of REPTree.

Capability

Supported

Class

Nominal class, Numeric class, Missing class values, Binary class, Date class

Attributes

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

Min # of instances

1

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