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Class weka.classifiers.meta.LogitBoost 

java.lang.Object
    |
    +----weka.classifiers.Classifier
            |
            +----weka.classifiers.DistributionClassifier
                    |
                    +----weka.classifiers.meta.LogitBoost
public class LogitBoost
extends DistributionClassifier
implements OptionHandler, Sourcable, WeightedInstancesHandler
Class for boosting any classifier that can handle weighted instances. This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

Friedman, J., T. Hastie and R. Tibshirani (1998) Additive Logistic Regression: a Statistical View of Boosting download postscript.

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a weak learner as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-F num
Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

-R num
Set number of runs for the internal cross-validation (default 1).

-L num
Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

-H num
Set the value of the shrinkage parameter (default 1).

Options after -- are passed to the designated learner.

Version:
$Revision: 1.26 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)

Constructor Index

o LogitBoost()
 

Method Index

o buildClassifier(Instances)
Builds the boosted classifier
o classifiers()
Returns the array of classifiers that have been built.
o distributionForInstance(Instance)
Calculates the class membership probabilities for the given test instance.
o getClassifier()
Get the classifier used as the classifier
o getDebug()
Get whether debugging is turned on
o getLikelihoodThreshold()
Get the value of Precision.
o getMaxIterations()
Get the maximum number of boost iterations
o getNumFolds()
Get the value of NumFolds.
o getNumRuns()
Get the value of NumRuns.
o getOptions()
Gets the current settings of the Classifier.
o getSeed()
Get seed for resampling.
o getShrinkage()
Get the value of Shrinkage.
o getUseResampling()
Get whether resampling is turned on
o getWeightThreshold()
Get the degree of weight thresholding
o listOptions()
Returns an enumeration describing the available options.
o main(String[])
Main method for testing this class.
o setClassifier(Classifier)
Set the classifier for boosting.
o setDebug(boolean)
Set debugging mode
o setLikelihoodThreshold(double)
Set the value of Precision.
o setMaxIterations(int)
Set the maximum number of boost iterations
o setNumFolds(int)
Set the value of NumFolds.
o setNumRuns(int)
Set the value of NumRuns.
o setOptions(String[])
Parses a given list of options.
o setSeed(int)
Set seed for resampling.
o setShrinkage(double)
Set the value of Shrinkage.
o setUseResampling(boolean)
Set resampling mode
o setWeightThreshold(int)
Set weight thresholding
o toSource(String)
Returns the boosted model as Java source code.
o toString()
Returns description of the boosted classifier.

Constructor Detail

o LogitBoost
public LogitBoost()

Method Detail

o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
Returns:
an enumeration of all the available options.
o setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
          Parses a given list of options. Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a weak learner as the basis for boosting (required).

-I num
Set the number of boost iterations (default 10).

-Q
Use resampling instead of reweighting.

-S seed
Random number seed for resampling (default 1).

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-F num
Set number of folds for the internal cross-validation (default 0 -- no cross-validation).

-R num
Set number of runs for the internal cross-validation (default 1.

-L num
Set the threshold for the improvement of the average loglikelihood (default -Double.MAX_VALUE).

-H num
Set the value of the shrinkage parameter (default 1).

Options after -- are passed to the designated learner.

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
Returns:
an array of strings suitable for passing to setOptions
o getShrinkage
public double getShrinkage()
          Get the value of Shrinkage.
Returns:
Value of Shrinkage.
o setShrinkage
public void setShrinkage(double newShrinkage)
          Set the value of Shrinkage.
Parameters:
newShrinkage - Value to assign to Shrinkage.
o getLikelihoodThreshold
public double getLikelihoodThreshold()
          Get the value of Precision.
Returns:
Value of Precision.
o setLikelihoodThreshold
public void setLikelihoodThreshold(double newPrecision)
          Set the value of Precision.
Parameters:
newPrecision - Value to assign to Precision.
o getNumRuns
public int getNumRuns()
          Get the value of NumRuns.
Returns:
Value of NumRuns.
o setNumRuns
public void setNumRuns(int newNumRuns)
          Set the value of NumRuns.
Parameters:
newNumRuns - Value to assign to NumRuns.
o getNumFolds
public int getNumFolds()
          Get the value of NumFolds.
Returns:
Value of NumFolds.
o setNumFolds
public void setNumFolds(int newNumFolds)
          Set the value of NumFolds.
Parameters:
newNumFolds - Value to assign to NumFolds.
o setUseResampling
public void setUseResampling(boolean r)
          Set resampling mode
Parameters:
resampling - true if resampling should be done
o getUseResampling
public boolean getUseResampling()
          Get whether resampling is turned on
Returns:
true if resampling output is on
o setSeed
public void setSeed(int seed)
          Set seed for resampling.
Parameters:
seed - the seed for resampling
o getSeed
public int getSeed()
          Get seed for resampling.
Returns:
the seed for resampling
o setClassifier
public void setClassifier(Classifier newClassifier)
          Set the classifier for boosting. The learner should be able to handle numeric class attributes.
Parameters:
newClassifier - the Classifier to use.
o getClassifier
public Classifier getClassifier()
          Get the classifier used as the classifier
Returns:
the classifier used as the classifier
o setMaxIterations
public void setMaxIterations(int maxIterations)
          Set the maximum number of boost iterations
Parameters:
maxIterations - the maximum number of boost iterations
o getMaxIterations
public int getMaxIterations()
          Get the maximum number of boost iterations
Returns:
the maximum number of boost iterations
o setWeightThreshold
public void setWeightThreshold(int threshold)
          Set weight thresholding
Parameters:
thresholding - the percentage of weight mass used for training
o getWeightThreshold
public int getWeightThreshold()
          Get the degree of weight thresholding
Returns:
the percentage of weight mass used for training
o setDebug
public void setDebug(boolean debug)
          Set debugging mode
Parameters:
debug - true if debug output should be printed
o getDebug
public boolean getDebug()
          Get whether debugging is turned on
Returns:
true if debugging output is on
o buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
          Builds the boosted classifier
Overrides:
buildClassifier in class Classifier
o classifiers
public Classifier[][] classifiers()
          Returns the array of classifiers that have been built.
o distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
          Calculates the class membership probabilities for the given test instance.
Parameters:
instance - the instance to be classified
Returns:
predicted class probability distribution
Throws:
java.lang.Exception - if instance could not be classified successfully
Overrides:
distributionForInstance in class DistributionClassifier
o toSource
public java.lang.String toSource(java.lang.String className) throws java.lang.Exception
          Returns the boosted model as Java source code.
Returns:
the tree as Java source code
Throws:
java.lang.Exception - if something goes wrong
o toString
public java.lang.String toString()
          Returns description of the boosted classifier.
Returns:
description of the boosted classifier as a string
Overrides:
toString in class java.lang.Object
o main
public static void main(java.lang.String argv[])
          Main method for testing this class.
Parameters:
argv - the options 
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