weka.classifiers.trees

Class DecisionStump

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.trees.DecisionStump

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class DecisionStump
extends AbstractClassifier
implements WeightedInstancesHandler, Sourcable

Class for building and using a decision stump. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or classification (based on entropy). Missing is treated as a separate value.

Typical usage:

java weka.classifiers.meta.LogitBoost -I 100 -W weka.classifiers.trees.DecisionStump -t training_data

Valid options are:

  -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected int	m_AttIndex The attribute used for classification.
protected double[][]	m_Distribution The distribution of class values or the means in each subset.
protected Instances	m_Instances The instances used for training.
protected double	m_SplitPoint The split point (index respectively).
protected Classifier	m_ZeroR a ZeroR model in case no model can be built from the data

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
DecisionStump()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
protected double	findSplitNominal(int index) Finds best split for nominal attribute and returns value.
protected double	findSplitNominalNominal(int index) Finds best split for nominal attribute and nominal class and returns value.
protected double	findSplitNominalNumeric(int index) Finds best split for nominal attribute and numeric class and returns value.
protected double	findSplitNumeric(int index) Finds best split for numeric attribute and returns value.
protected double	findSplitNumericNominal(int index) Finds best split for numeric attribute and nominal class and returns value.
protected double	findSplitNumericNumeric(int index) Finds best split for numeric attribute and numeric class and returns value.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing classifier
static void	main(java.lang.String[] argv) Main method for testing this class.
protected java.lang.String	printClass(double[] dist) Prints a classification.
protected java.lang.String	printDist(double[] dist) Prints a class distribution.
protected java.lang.String	sourceClass(Attribute c, double[] dist) Returns the value as string out of the given distribution
java.lang.String	toSource(java.lang.String className) Returns the decision tree as Java source code.
java.lang.String	toString() Returns a description of the classifier.
protected double	variance(double[][] s, double[] sS, double[] sumOfWeights) Computes variance for subsets.
protected int	whichSubset(Instance instance) Returns the subset an instance falls into.

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, setOptions

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_AttIndex

protected int m_AttIndex

The attribute used for classification.

m_SplitPoint

protected double m_SplitPoint

The split point (index respectively).

m_Distribution

protected double[][] m_Distribution

The distribution of class values or the means in each subset.

m_Instances

protected Instances m_Instances

The instances used for training.

m_ZeroR

protected Classifier m_ZeroR

a ZeroR model in case no model can be built from the data

Constructor Detail

DecisionStump

public DecisionStump()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

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 instances)
                     throws java.lang.Exception

Generates the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

instances - set of instances serving as training data

Throws:

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

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns the decision tree as Java source code.

Specified by:

toSource in interface Sourcable

Parameters:

className - the classname of the generated code

Returns:

the tree as Java source code

Throws:

java.lang.Exception - if something goes wrong

sourceClass

protected java.lang.String sourceClass(Attribute c,
                                       double[] dist)

Returns the value as string out of the given distribution

Parameters:

c - the attribute to get the value for

dist - the distribution to extract the value

Returns:

the value

toString

public java.lang.String toString()

Returns a description of the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a description of the classifier as a string.

printDist

protected java.lang.String printDist(double[] dist)
                              throws java.lang.Exception

Prints a class distribution.

Parameters:

dist - the class distribution to print

Returns:

the distribution as a string

Throws:

java.lang.Exception - if distribution can't be printed

printClass

protected java.lang.String printClass(double[] dist)
                               throws java.lang.Exception

Prints a classification.

Parameters:

dist - the class distribution

Returns:

the classificationn as a string

Throws:

java.lang.Exception - if the classification can't be printed

findSplitNominal

protected double findSplitNominal(int index)
                           throws java.lang.Exception

Finds best split for nominal attribute and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

findSplitNominalNominal

protected double findSplitNominalNominal(int index)
                                  throws java.lang.Exception

Finds best split for nominal attribute and nominal class and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

findSplitNominalNumeric

protected double findSplitNominalNumeric(int index)
                                  throws java.lang.Exception

Finds best split for nominal attribute and numeric class and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

findSplitNumeric

protected double findSplitNumeric(int index)
                           throws java.lang.Exception

Finds best split for numeric attribute and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

findSplitNumericNominal

protected double findSplitNumericNominal(int index)
                                  throws java.lang.Exception

Finds best split for numeric attribute and nominal class and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

findSplitNumericNumeric

protected double findSplitNumericNumeric(int index)
                                  throws java.lang.Exception

Finds best split for numeric attribute and numeric class and returns value.

Parameters:

index - attribute index

Returns:

value of criterion for the best split

Throws:

java.lang.Exception - if something goes wrong

variance

protected double variance(double[][] s,
                          double[] sS,
                          double[] sumOfWeights)

Computes variance for subsets.

Parameters:

s -

sS -

sumOfWeights -

Returns:

the variance

whichSubset

protected int whichSubset(Instance instance)
                   throws java.lang.Exception

Returns the subset an instance falls into.

Parameters:

instance - the instance to check

Returns:

the subset the instance falls into

Throws:

java.lang.Exception - if something goes wrong

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - the options