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