Package weka.classifiers.meta
Class LogitBoost
- All Implemented Interfaces:
Serializable
,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:
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 consoleOptions 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:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances data) Builds the boosted classifierClassifier[][]
Returns the array of classifiers that have been built.double[]
distributionForInstance
(Instance instance) Calculates the class membership probabilities for the given test instance.Returns default capabilities of the classifier.double
Get the value of Precision.int
Get the value of NumFolds.int
Get the value of NumRuns.String[]
Gets the current settings of the Classifier.Returns the revision string.double
Get the value of Shrinkage.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
Get whether resampling is turned onint
Get the degree of weight thresholdingReturns a string describing classifierReturns the tip text for this propertyReturns an enumeration describing the available options.static void
Main method for testing this class.Returns the tip text for this propertyReturns 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
(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 thresholdingReturns the tip text for this propertyReturns the boosted model as Java source code.toString()
Returns description of the boosted classifier.Returns the tip text for this propertyReturns the tip text for this propertyMethods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Constructor Details
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LogitBoost
public LogitBoost()Constructor.
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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
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
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:
Exception
- if an option is not supported
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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
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
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
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
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
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
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
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Builds the boosted classifier- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the data to train the classifier with- Throws:
Exception
- if building fails, e.g., can't handle data
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classifiers
Returns the array of classifiers that have been built.- Returns:
- the built classifiers
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distributionForInstance
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:
Exception
- if instance could not be classified successfully
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toSource
Returns the boosted model as Java source code. -
toString
Returns description of the boosted classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the options
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