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Class weka.classifiers.trees.m5.M5Base

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.trees.m5.M5Base

public abstract class M5Base

extends Classifier

implements OptionHandler, AdditionalMeasureProducer

Valid options are:

-U
Use unsmoothed predictions.

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

Version:

$Revision: 1.7 $

M5Base()

Constructor

buildClassifier(Instances)

Generates the classifier.

classifyInstance(Instance)

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

enumerateMeasures()

Returns an enumeration of the additional measure names

getBuildRegressionTree()

Get the value of regressionTree.

getMeasure(String)

Returns the value of the named measure

getMinNumInstances()

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

getOptions()

Gets the current settings of the classifier.

getUnpruned()

Get whether unpruned tree/rules are being generated

getUseUnsmoothed()

Get whether or not smoothing is being used

listOptions()

Returns an enumeration describing the available options

measureNumRules()

return the number of rules

setBuildRegressionTree(boolean)

Set the value of regressionTree.

setMinNumInstances(double)

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

setOptions(String[])

Parses a given list of options.

setUnpruned(boolean)

Use unpruned tree/rules

setUseUnsmoothed(boolean)

Use unsmoothed predictions

toString()

Returns a description of the classifier

public M5Base()

          Constructor

public java.util.Enumeration listOptions()

          Returns an enumeration describing the available options

Returns:

an enumeration of all the available options

public void setOptions(java.lang.String options[]) throws java.lang.Exception

          Parses a given list of options.

Valid options are:

-U
Use unsmoothed predictions.

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

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

public java.lang.String[] getOptions()

          Gets the current settings of the classifier.

Returns:

an array of strings suitable for passing to setOptions

public void setUnpruned(boolean unpruned)

          Use unpruned tree/rules

Parameters:

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

public boolean getUnpruned()

          Get whether unpruned tree/rules are being generated

Returns:

true if unpruned tree/rules are to be generated

public void setUseUnsmoothed(boolean s)

          Use unsmoothed predictions

Parameters:

s - true if unsmoothed predictions are to be used

public boolean getUseUnsmoothed()

          Get whether or not smoothing is being used

Returns:

true if unsmoothed predictions are to be used

public boolean getBuildRegressionTree()

          Get the value of regressionTree.

Returns:

Value of regressionTree.

public void setBuildRegressionTree(boolean newregressionTree)

          Set the value of regressionTree.

Parameters:

newregressionTree - Value to assign to regressionTree.

public void setMinNumInstances(double minNum)

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

Parameters:

minNum - the minimum number of instances

public double getMinNumInstances()

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

Returns:

a double value

public void buildClassifier(Instances data) throws java.lang.Exception

          Generates the classifier.

Parameters:

data - set of instances serving as training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

Overrides:

buildClassifier in class Classifier

public double classifyInstance(Instance inst) throws java.lang.Exception

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

Parameters:

inst - the instance whos class value is to be predicted

Returns:

the prediction

Throws:

if - a prediction can't be made.

Overrides:

classifyInstance in class Classifier

public java.lang.String toString()

          Returns a description of the classifier

Returns:

a description of the classifier as a String

Overrides:

toString in class java.lang.Object

public java.util.Enumeration enumerateMeasures()

          Returns an enumeration of the additional measure names

Returns:

an enumeration of the measure names

public double getMeasure(java.lang.String additionalMeasureName)

          Returns the value of the named measure

Parameters:

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

Returns:

the value of the named measure

Throws:

java.lang.Exception - if the named measure is not supported

public double measureNumRules()

          return the number of rules

Returns:

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

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