M5Rules
Package
weka.classifiers.rules
Synopsis
Generates a decision list for regression problems using separate-and-conquer. In each iteration it builds a model tree using M5 and makes the "best" leaf into a rule.
For more information see:
Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999.
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.
Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997.
Options
The table below describes the options available for M5Rules.
Option |
Description |
|---|---|
buildRegressionTree |
Whether to generate a regression tree/rule instead of a model tree/rule. |
debug |
If set to true, classifier may output additional info to the console. |
minNumInstances |
The minimum number of instances to allow at a leaf node. |
unpruned |
Whether unpruned tree/rules are to be generated. |
useUnsmoothed |
Whether to use unsmoothed predictions. |
Capabilities
The table below describes the capabilites of M5Rules.
Capability |
Supported |
|---|---|
Class |
Date class, Missing class values, Numeric class |
Attributes |
Nominal attributes, Unary attributes, Missing values, Date attributes, Binary attributes, Numeric attributes, Empty nominal attributes |
Min # of instances |
1 |