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Class weka.classifiers.trees.m5.Rule

java.lang.Object
    |
    +----weka.classifiers.trees.m5.Rule

public class Rule

extends java.lang.Object

Version:

$Revision: 1.5 $

Author:

Mark Hall

Rule()

Constructor declaration

buildClassifier(Instances)

Generates a single rule or m5 model tree.

classifyInstance(Instance)

Calculates a prediction for an instance using this rule or M5 model tree

getMinNumInstances()

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

getRegressionTree()

Get the value of regressionTree.

getSmoothing()

Get whether or not smoothing has been turned on

getUnpruned()

Get whether unpruned tree/rules are being generated

getUseTree()

get whether an m5 tree is being used rather than rules

notCoveredInstances()

Get the instances not covered by this rule

setMinNumInstances(double)

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

setRegressionTree(boolean)

Set the value of regressionTree.

setSmoothing(boolean)

Smooth predictions

setUnpruned(boolean)

Use unpruned tree/rules

setUseTree(boolean)

Use an m5 tree rather than generate rules

toString()

Return a description of the m5 tree or rule

public Rule()

          Constructor declaration

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

          Generates a single rule or m5 model tree.

Parameters:

data - set of instances serving as training data

Throws:

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

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

          Calculates a prediction for an instance using this rule or 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.

public java.lang.String toString()

          Return a description of the m5 tree or rule

Returns:

a description of the m5 tree or rule as a String

Overrides:

toString in class java.lang.Object

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 setUseTree(boolean u)

          Use an m5 tree rather than generate rules

Parameters:

u - true if m5 tree is to be used

public boolean getUseTree()

          get whether an m5 tree is being used rather than rules

Returns:

true if an m5 tree is being used.

public void setSmoothing(boolean s)

          Smooth predictions

Parameters:

s - true if smoothing is to be used

public boolean getSmoothing()

          Get whether or not smoothing has been turned on

Returns:

true if smoothing is being used

public Instances notCoveredInstances()

          Get the instances not covered by this rule

Returns:

the instances not covered

public boolean getRegressionTree()

          Get the value of regressionTree.

Returns:

Value of regressionTree.

public void setRegressionTree(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

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