Package weka.classifiers.meta
Class LogitBoost
- java.lang.Object
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,Sourcable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class LogitBoost extends RandomizableIteratedSingleClassifierEnhancer implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler
Class for performing additive logistic regression.
This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
Can do efficient internal cross-validation to determine appropriate number of iterations. BibTeX:@techreport{Friedman1998, address = {Stanford University}, author = {J. Friedman and T. Hastie and R. Tibshirani}, title = {Additive Logistic Regression: a Statistical View of Boosting}, year = {1998}, PS = {http://www-stat.stanford.edu/\~jhf/ftp/boost.ps} }
Valid options are:-Q Use resampling instead of reweighting for boosting.
-P <percent> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-F <num> Number of folds for internal cross-validation. (default 0 -- no cross-validation)
-R <num> Number of runs for internal cross-validation. (default 1)
-L <num> Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE)
-H <num> Shrinkage parameter. (default 1)
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the console
Options after -- are passed to the designated learner.- Version:
- $Revision: 9371 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description LogitBoost()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
Builds the boosted classifierClassifier[][]
classifiers()
Returns the array of classifiers that have been built.double[]
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.Capabilities
getCapabilities()
Returns default capabilities of the classifier.double
getLikelihoodThreshold()
Get the value of Precision.int
getNumFolds()
Get the value of NumFolds.int
getNumRuns()
Get the value of NumRuns.java.lang.String[]
getOptions()
Gets the current settings of the Classifier.java.lang.String
getRevision()
Returns the revision string.double
getShrinkage()
Get the value of Shrinkage.TechnicalInformation
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.boolean
getUseResampling()
Get whether resampling is turned onint
getWeightThreshold()
Get the degree of weight thresholdingjava.lang.String
globalInfo()
Returns a string describing classifierjava.lang.String
likelihoodThresholdTipText()
Returns the tip text for this propertyjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
numFoldsTipText()
Returns the tip text for this propertyjava.lang.String
numRunsTipText()
Returns the tip text for this propertyvoid
setLikelihoodThreshold(double newPrecision)
Set the value of Precision.void
setNumFolds(int newNumFolds)
Set the value of NumFolds.void
setNumRuns(int newNumRuns)
Set the value of NumRuns.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setShrinkage(double newShrinkage)
Set the value of Shrinkage.void
setUseResampling(boolean r)
Set resampling modevoid
setWeightThreshold(int threshold)
Set weight thresholdingjava.lang.String
shrinkageTipText()
Returns the tip text for this propertyjava.lang.String
toSource(java.lang.String className)
Returns the boosted model as Java source code.java.lang.String
toString()
Returns description of the boosted classifier.java.lang.String
useResamplingTipText()
Returns the tip text for this propertyjava.lang.String
weightThresholdTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
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Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-Q Use resampling instead of reweighting for boosting.
-P <percent> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-F <num> Number of folds for internal cross-validation. (default 0 -- no cross-validation)
-R <num> Number of runs for internal cross-validation. (default 1)
-L <num> Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE)
-H <num> Shrinkage parameter. (default 1)
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the console
Options after -- are passed to the designated learner.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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shrinkageTipText
public java.lang.String shrinkageTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getShrinkage
public double getShrinkage()
Get the value of Shrinkage.- Returns:
- Value of Shrinkage.
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setShrinkage
public void setShrinkage(double newShrinkage)
Set the value of Shrinkage.- Parameters:
newShrinkage
- Value to assign to Shrinkage.
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likelihoodThresholdTipText
public java.lang.String likelihoodThresholdTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getLikelihoodThreshold
public double getLikelihoodThreshold()
Get the value of Precision.- Returns:
- Value of Precision.
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setLikelihoodThreshold
public void setLikelihoodThreshold(double newPrecision)
Set the value of Precision.- Parameters:
newPrecision
- Value to assign to Precision.
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numRunsTipText
public java.lang.String numRunsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNumRuns
public int getNumRuns()
Get the value of NumRuns.- Returns:
- Value of NumRuns.
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setNumRuns
public void setNumRuns(int newNumRuns)
Set the value of NumRuns.- Parameters:
newNumRuns
- Value to assign to NumRuns.
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numFoldsTipText
public java.lang.String numFoldsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNumFolds
public int getNumFolds()
Get the value of NumFolds.- Returns:
- Value of NumFolds.
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setNumFolds
public void setNumFolds(int newNumFolds)
Set the value of NumFolds.- Parameters:
newNumFolds
- Value to assign to NumFolds.
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useResamplingTipText
public java.lang.String useResamplingTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setUseResampling
public void setUseResampling(boolean r)
Set resampling mode- Parameters:
r
- true if resampling should be done
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getUseResampling
public boolean getUseResampling()
Get whether resampling is turned on- Returns:
- true if resampling output is on
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weightThresholdTipText
public java.lang.String weightThresholdTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setWeightThreshold
public void setWeightThreshold(int threshold)
Set weight thresholding- Parameters:
threshold
- the percentage of weight mass used for training
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getWeightThreshold
public int getWeightThreshold()
Get the degree of weight thresholding- Returns:
- the percentage of weight mass used for training
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Builds the boosted classifier- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the data to train the classifier with- Throws:
java.lang.Exception
- if building fails, e.g., can't handle data
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classifiers
public Classifier[][] classifiers()
Returns the array of classifiers that have been built.- Returns:
- the built classifiers
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
java.lang.Exception
- if instance could not be classified successfully
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toSource
public java.lang.String toSource(java.lang.String className) throws java.lang.Exception
Returns the boosted model as Java source code.
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toString
public java.lang.String toString()
Returns description of the boosted classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the boosted classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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