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 |