REPTree

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