weka.classifiers.trees.m5
Class M5Base

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.trees.m5.M5Base

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

Direct Known Subclasses:

M5P, M5Rules

public abstract class M5Base
extends AbstractClassifier
implements AdditionalMeasureProducer, TechnicalInformationHandler

M5Base. Implements base routines for generating M5 Model trees and rules.

The original algorithm M5 was invented by Quinlan:
Quinlan J. R. (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence. 343--348. World Scientific, Singapore.

Yong Wang made improvements and created M5':
Wang, Y and Witten, I. H. (1997). Induction of model trees for predicting continuous classes. Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.

Valid options are:

-U
Use unsmoothed predictions.

-R
Build regression tree/rule rather than model tree/rule

Version:

$Revision: 8034 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

M5Base()
Constructor

Method Summary

void	buildClassifier(Instances data) Generates the classifier.
String	buildRegressionTreeTipText() Returns the tip text for this property
double	classifyInstance(Instance inst) Calculates a prediction for an instance using a set of rules or an M5 model tree
Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names
String	generateRulesTipText() Returns the tip text for this property
boolean	getBuildRegressionTree() Get the value of regressionTree.
Capabilities	getCapabilities() Returns default capabilities of the classifier, i.e., of LinearRegression.
RuleNode	getM5RootNode()
double	getMeasure(String additionalMeasureName) Returns the value of the named measure
double	getMinNumInstances() Get the minimum number of instances to allow at a leaf node
String[]	getOptions() Gets the current settings of the classifier.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
boolean	getUnpruned() Get whether unpruned tree/rules are being generated
boolean	getUseUnsmoothed() Get whether or not smoothing is being used
String	globalInfo() returns information about the classifier
Enumeration	listOptions() Returns an enumeration describing the available options
double	measureNumRules() return the number of rules
String	minNumInstancesTipText() Returns the tip text for this property
void	setBuildRegressionTree(boolean newregressionTree) Set the value of regressionTree.
void	setMinNumInstances(double minNum) Set the minimum number of instances to allow at a leaf node
void	setOptions(String[] options) Parses a given list of options.
void	setUnpruned(boolean unpruned) Use unpruned tree/rules
void	setUseUnsmoothed(boolean s) Use unsmoothed predictions
String	toString() Returns a description of the classifier
String	unprunedTipText() Returns the tip text for this property
String	useUnsmoothedTipText() Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

M5Base

public M5Base()

Constructor

Method Detail

globalInfo

public String globalInfo()

returns information about the classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClassifier

Returns:

an enumeration of all the available options

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

-U
Use unsmoothed predictions.

-R
Build a regression tree rather than a model tree.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

unprunedTipText

public String unprunedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setUnpruned

public void setUnpruned(boolean unpruned)

Use unpruned tree/rules

Parameters:

unpruned - true if unpruned tree/rules are to be generated

getUnpruned

public boolean getUnpruned()

Get whether unpruned tree/rules are being generated

Returns:

true if unpruned tree/rules are to be generated

generateRulesTipText

public String generateRulesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

useUnsmoothedTipText

public String useUnsmoothedTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setUseUnsmoothed

public void setUseUnsmoothed(boolean s)

Use unsmoothed predictions

Parameters:

s - true if unsmoothed predictions are to be used

getUseUnsmoothed

public boolean getUseUnsmoothed()

Get whether or not smoothing is being used

Returns:

true if unsmoothed predictions are to be used

buildRegressionTreeTipText

public String buildRegressionTreeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getBuildRegressionTree

public boolean getBuildRegressionTree()

Get the value of regressionTree.

Returns:

Value of regressionTree.

setBuildRegressionTree

public void setBuildRegressionTree(boolean newregressionTree)

Set the value of regressionTree.

Parameters:

newregressionTree - Value to assign to regressionTree.

minNumInstancesTipText

public String minNumInstancesTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinNumInstances

public void setMinNumInstances(double minNum)

Set the minimum number of instances to allow at a leaf node

Parameters:

minNum - the minimum number of instances

getMinNumInstances

public double getMinNumInstances()

Get the minimum number of instances to allow at a leaf node

Returns:

a double value

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier, i.e., of LinearRegression.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - set of instances serving as training data

Throws:

Exception - if the classifier has not been generated successfully

classifyInstance

public double classifyInstance(Instance inst)
                        throws Exception

Calculates a prediction for an instance using a set of rules or an M5 model tree

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

inst - the instance whos class value is to be predicted

Returns:

the prediction

Throws:

Exception - if a prediction can't be made.

toString

public String toString()

Returns a description of the classifier

Overrides:

toString in class Object

Returns:

a description of the classifier as a String

enumerateMeasures

public Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(String additionalMeasureName)

Returns the value of the named measure

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

Exception - if the named measure is not supported

measureNumRules

public double measureNumRules()

return the number of rules

Returns:

the number of rules (same as # linear models & # leaves in the tree)

getM5RootNode

public RuleNode getM5RootNode()