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Class weka.classifiers.j48.PART 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.j48.PART
public class PART
extends DistributionClassifier
implements OptionHandler, WeightedInstancesHandler, Summarizable
Class for generating a PART decision list. For more information, see

Eibe Frank and Ian H. Witten (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., ed., Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers, San Francisco, CA.

Valid options are:

-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.

-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.

Version:
$Revision: 1.9 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)

Constructor Index

o PART()

Method Index

o buildClassifier(Instances)
Generates the classifier.
o classifyInstance(Instance)
Classifies an instance.
o distributionForInstance(Instance)
Returns class probabilities for an instance.
o getBinarySplits()
Get the value of binarySplits.
o getConfidenceFactor()
Get the value of CF.
o getMinNumObj()
Get the value of minNumObj.
o getNumFolds()
Get the value of numFolds.
o getOptions()
Gets the current settings of the Classifier.
o getReducedErrorPruning()
Get the value of reducedErrorPruning.
o listOptions()
Returns an enumeration describing the available options Valid options are:

-C confidence
Set confidence threshold for pruning.

o main(String[])
Main method for testing this class.
o setBinarySplits(boolean)
Set the value of binarySplits.
o setConfidenceFactor(float)
Set the value of CF.
o setMinNumObj(int)
Set the value of minNumObj.
o setNumFolds(int)
Set the value of numFolds.
o setOptions(String[])
Parses a given list of options.
o setReducedErrorPruning(boolean)
Set the value of reducedErrorPruning.
o toString()
Returns a description of the classifier
o toSummaryString()
Returns a superconcise version of the model

Constructors

o PART 
 public PART()

Methods

o buildClassifier 
 public void buildClassifier(Instances instances) throws Exception
Generates the classifier.

Throws: Exception
if classifier can't be built successfully
Overrides:
buildClassifier in class Classifier
o classifyInstance 
 public double classifyInstance(Instance instance) throws Exception
Classifies an instance.

Throws: Exception
if instance can't be classified successfully
Overrides:
classifyInstance in class DistributionClassifier
o distributionForInstance 
 public final double[] distributionForInstance(Instance instance) throws Exception
Returns class probabilities for an instance.

Throws: Exception
if the distribution can't be computed successfully
Overrides:
distributionForInstance in class DistributionClassifier
o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options Valid options are:

-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.

-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.

Returns:
an enumeration of all the available options
o setOptions 
 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
o getOptions 
 public String[] getOptions()
Gets the current settings of the Classifier.

Returns:
an array of strings suitable for passing to setOptions
o toString 
 public String toString()
Returns a description of the classifier

Overrides:
toString in class Object
o toSummaryString 
 public String toSummaryString()
Returns a superconcise version of the model

o getConfidenceFactor 
 public float getConfidenceFactor()
Get the value of CF.

Returns:
Value of CF.
o setConfidenceFactor 
 public void setConfidenceFactor(float v)
Set the value of CF.

Parameters:
v - Value to assign to CF.
o getMinNumObj 
 public int getMinNumObj()
Get the value of minNumObj.

Returns:
Value of minNumObj.
o setMinNumObj 
 public void setMinNumObj(int v)
Set the value of minNumObj.

Parameters:
v - Value to assign to minNumObj.
o getReducedErrorPruning 
 public boolean getReducedErrorPruning()
Get the value of reducedErrorPruning.

Returns:
Value of reducedErrorPruning.
o setReducedErrorPruning 
 public void setReducedErrorPruning(boolean v)
Set the value of reducedErrorPruning.

Parameters:
v - Value to assign to reducedErrorPruning.
o getNumFolds 
 public int getNumFolds()
Get the value of numFolds.

Returns:
Value of numFolds.
o setNumFolds 
 public void setNumFolds(int v)
Set the value of numFolds.

Parameters:
v - Value to assign to numFolds.
o getBinarySplits 
 public boolean getBinarySplits()
Get the value of binarySplits.

Returns:
Value of binarySplits.
o setBinarySplits 
 public void setBinarySplits(boolean v)
Set the value of binarySplits.

Parameters:
v - Value to assign to binarySplits.
o main 
 public static void main(String argv[])
Main method for testing this class.

Parameters:
String - options 
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