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Class weka.classifiers.j48.J48

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.j48.J48

public class J48

extends DistributionClassifier

implements OptionHandler, Drawable, Matchable, Sourcable, WeightedInstancesHandler, Summarizable

Ross Quinlan (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA.

-U
Use unpruned tree.

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning. No subtree raising is performed.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

-S
Don't perform subtree raising.

Version:

$Revision: 1.12 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

J48()

buildClassifier(Instances)

Generates the classifier.

classifyInstance(Instance)

Classifies an instance.

distributionForInstance(Instance)

Returns class probabilities for an instance.

getBinarySplits()

Get the value of binarySplits.

getConfidenceFactor()

Get the value of CF.

getMinNumObj()

Get the value of minNumObj.

getNumFolds()

Get the value of numFolds.

getOptions()

Gets the current settings of the Classifier.

getReducedErrorPruning()

Get the value of reducedErrorPruning.

getSubtreeRaising()

Get the value of subtreeRaising.

getUnpruned()

Get the value of unpruned.

graph()

Returns graph describing the tree.

listOptions()

Returns an enumeration describing the available options Valid options are:

-U
Use unpruned tree.

-C confidence
Set confidence threshold for pruning.

main(String[])

Main method for testing this class

prefix()

Returns tree in prefix order.

setBinarySplits(boolean)

Set the value of binarySplits.

setConfidenceFactor(float)

Set the value of CF.

setMinNumObj(int)

Set the value of minNumObj.

setNumFolds(int)

Set the value of numFolds.

setOptions(String[])

Parses a given list of options.

setReducedErrorPruning(boolean)

Set the value of reducedErrorPruning.

setSubtreeRaising(boolean)

Set the value of subtreeRaising.

setUnpruned(boolean)

Set the value of unpruned.

toSource(String)

Returns tree as an if-then statement.

toString()

Returns a description of the classifier.

toSummaryString()

Returns a superconcise version of the model

 public J48()

 public void buildClassifier(Instances instances) throws Exception

Generates the classifier.

Throws: Exception

if classifier can't be built successfully

Overrides:

buildClassifier in class Classifier

 public double classifyInstance(Instance instance) throws Exception

Classifies an instance.

Throws: Exception

if instance can't be classified successfully

Overrides:

classifyInstance in class DistributionClassifier

 public final double[] distributionForInstance(Instance instance) throws Exception

Returns class probabilities for an instance.

Throws: Exception

if distribution can't be computed successfully

Overrides:

distributionForInstance in class DistributionClassifier

 public String graph() throws Exception

Returns graph describing the tree.

Throws: Exception

if graph can't be computed

 public String prefix() throws Exception

Returns tree in prefix order.

Throws: Exception

if something goes wrong

 public String toSource(String className) throws Exception

Returns tree as an if-then statement.

Returns:

the tree as a Java if-then type statement

Throws: Exception

if something goes wrong

 public Enumeration listOptions()

Returns an enumeration describing the available options Valid options are:

-U
Use unpruned tree.

-C confidence
Set confidence threshold for pruning. (Default: 0.25)

-M number
Set minimum number of instances per leaf. (Default: 2)

-R
Use reduced error pruning. No subtree raising is performed.

-N number
Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

-B
Use binary splits for nominal attributes.

-S
Don't perform subtree raising.

Returns:

an enumeration of all the available options

 public void setOptions(String options[]) throws Exception

Parses a given list of options.

Parameters:

options - the list of options as an array of strings

Throws: Exception

if an option is not supported

 public String[] getOptions()

Gets the current settings of the Classifier.

Returns:

an array of strings suitable for passing to setOptions

 public String toString()

Returns a description of the classifier.

Overrides:

toString in class Object

 public String toSummaryString()

Returns a superconcise version of the model

 public boolean getUnpruned()

Get the value of unpruned.

Returns:

Value of unpruned.

 public void setUnpruned(boolean v)

Set the value of unpruned.

Parameters:

v - Value to assign to unpruned.

 public float getConfidenceFactor()

Get the value of CF.

Returns:

Value of CF.

 public void setConfidenceFactor(float v)

Set the value of CF.

Parameters:

v - Value to assign to CF.

 public int getMinNumObj()

Get the value of minNumObj.

Returns:

Value of minNumObj.

 public void setMinNumObj(int v)

Set the value of minNumObj.

Parameters:

v - Value to assign to minNumObj.

 public boolean getReducedErrorPruning()

Get the value of reducedErrorPruning.

Returns:

Value of reducedErrorPruning.

 public void setReducedErrorPruning(boolean v)

Set the value of reducedErrorPruning.

Parameters:

v - Value to assign to reducedErrorPruning.

 public int getNumFolds()

Get the value of numFolds.

Returns:

Value of numFolds.

 public void setNumFolds(int v)

Set the value of numFolds.

Parameters:

v - Value to assign to numFolds.

 public boolean getBinarySplits()

Get the value of binarySplits.

Returns:

Value of binarySplits.

 public void setBinarySplits(boolean v)

Set the value of binarySplits.

Parameters:

v - Value to assign to binarySplits.

 public boolean getSubtreeRaising()

Get the value of subtreeRaising.

Returns:

Value of subtreeRaising.

 public void setSubtreeRaising(boolean v)

Set the value of subtreeRaising.

Parameters:

v - Value to assign to subtreeRaising.

 public static void main(String argv[])

Main method for testing this class

Parameters:

String - options

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