AdditiveRegression

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.meta.AdditiveRegression

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class AdditiveRegression
extends IteratedSingleClassifierEnhancer
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier

Meta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left by the classifier on the previous iteration. Prediction is accomplished by adding the predictions of each classifier. Reducing the shrinkage (learning rate) parameter helps prevent overfitting and has a smoothing effect but increases the learning time.

For more information see:

J.H. Friedman (1999). Stochastic Gradient Boosting.

BibTeX:

 @techreport{Friedman1999,
    author = {J.H. Friedman},
    institution = {Stanford University},
    title = {Stochastic Gradient Boosting},
    year = {1999},
    PS = {http://www-stat.stanford.edu/\~jhf/ftp/stobst.ps}
 }

Valid options are:

 -S
  Specify shrinkage rate. (default = 1.0, ie. no shrinkage)

 -I <num>
  Number of iterations.
  (default 10)

 -A
  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
 
  -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

Version:: $Revision: 15520 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description
`AdditiveRegression()` Default constructor specifying DecisionStump as the classifier
`AdditiveRegression(Classifier classifier)` Constructor which takes base classifier as argument.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Method used to build the classifier.
`double`	`classifyInstance(Instance inst)` Classify an instance.
`void`	`done()` Clean up.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`boolean`	`getMinimizeAbsoluteError()` Gets whether absolute error is to be minimized.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`boolean`	`getResume()` Returns true if the model is to be finalized (or has been finalized) after training.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getShrinkage()` Get the shrinkage rate.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing this attribute evaluator
`void`	`initializeClassifier(Instances data)` Initialize classifier.
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureNumIterations()` return the number of iterations (base classifiers) completed
`java.lang.String`	`minimizeAbsoluteErrorTipText()` Returns the tip text for this property
`boolean`	`next()` Perform another iteration.
`java.lang.String`	`resumeTipText()` Tool tip text for the resume property
`void`	`setMinimizeAbsoluteError(boolean f)` Sets whether absolute error is to be minimized.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setResume(boolean resume)` If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
`void`	`setShrinkage(double l)` Set the shrinkage parameter
`java.lang.String`	`shrinkageTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Returns textual description of the classifier.

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

Methods inherited from interface weka.classifiers.Classifier
distributionForInstance

- Constructor Detail
  - AdditiveRegression
```
public AdditiveRegression()
```
    Default constructor specifying DecisionStump as the classifier
  - AdditiveRegression
```
public AdditiveRegression(Classifier classifier)
```
    Constructor which takes base classifier as argument.
    
    Parameters:
    
    classifier - the base classifier to use
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing this attribute evaluator
    
    Returns:
    
    a description of the evaluator suitable for displaying in the explorer/experimenter gui
  - 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 interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class IteratedSingleClassifierEnhancer
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -S
  Specify shrinkage rate. (default = 1.0, ie. no shrinkage)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
```
```
 -A
  Minimize absolute error instead of squared error (assumes that base learner minimizes absolute error).
  
  -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
 
   
```
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class IteratedSingleClassifierEnhancer
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class IteratedSingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - 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
  - setShrinkage
```
public void setShrinkage(double l)
```
    Set the shrinkage parameter
    
    Parameters:
    
    l - the shrinkage rate.
  - getShrinkage
```
public double getShrinkage()
```
    Get the shrinkage rate.
    
    Returns:
    
    the value of the learning rate
  - minimizeAbsoluteErrorTipText
```
public java.lang.String minimizeAbsoluteErrorTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinimizeAbsoluteError
```
public void setMinimizeAbsoluteError(boolean f)
```
    Sets whether absolute error is to be minimized.
    
    Parameters:
    
    f - true if absolute error is to be minimized.
  - getMinimizeAbsoluteError
```
public boolean getMinimizeAbsoluteError()
```
    Gets whether absolute error is to be minimized.
    
    Returns:
    
    true if absolute error is to be minimized
  - resumeTipText
```
public java.lang.String resumeTipText()
```
    Tool tip text for the resume property
    
    Returns:
    
    the tool tip text for the finalize property
  - setResume
```
public void setResume(boolean resume)
```
    If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
    
    Specified by:
    
    setResume in interface IterativeClassifier
    
    Parameters:
    
    resume - true if the model is to be finalized after performing iterations
  - getResume
```
public boolean getResume()
```
    Returns true if the model is to be finalized (or has been finalized) after training.
    
    Specified by:
    
    getResume in interface IterativeClassifier
    
    Returns:
    
    the current value of finalize
  - 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 SingleClassifierEnhancer
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Method used to build the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Overrides:
    
    buildClassifier in class IteratedSingleClassifierEnhancer
    
    Parameters:
    
    data - the training data to be used for generating the bagged classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - initializeClassifier
```
public void initializeClassifier(Instances data)
                          throws java.lang.Exception
```
    Initialize classifier.
    
    Specified by:
    
    initializeClassifier in interface IterativeClassifier
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if the classifier could not be initialized successfully
  - next
```
public boolean next()
             throws java.lang.Exception
```
    Perform another iteration.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Returns:
    
    false if no further iterations could be performed, true otherwise
    
    Throws:
    
    java.lang.Exception - if this iteration fails for unexpected reasons
  - done
```
public void done()
```
    Clean up.
    
    Specified by:
    
    done in interface IterativeClassifier
  - classifyInstance
```
public double classifyInstance(Instance inst)
                        throws java.lang.Exception
```
    Classify an instance.
    
    Specified by:
    
    classifyInstance in interface Classifier
    
    Overrides:
    
    classifyInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to predict
    
    Returns:
    
    a prediction for the instance
    
    Throws:
    
    java.lang.Exception - if an error occurs
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - measureNumIterations
```
public double measureNumIterations()
```
    return the number of iterations (base classifiers) completed
    
    Returns:
    
    the number of iterations (same as number of base classifier models)
  - toString
```
public java.lang.String toString()
```
    Returns textual description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a description of the classifier as a string
  - 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 - should contain the following arguments: -t training file [-T test file] [-c class index]

Class AdditiveRegression

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Methods inherited from interface weka.classifiers.Classifier

Constructor Detail

AdditiveRegression

AdditiveRegression

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

shrinkageTipText

setShrinkage

getShrinkage

minimizeAbsoluteErrorTipText

setMinimizeAbsoluteError

getMinimizeAbsoluteError

resumeTipText

setResume

getResume

getCapabilities

buildClassifier

initializeClassifier

next

done

classifyInstance

enumerateMeasures

getMeasure

measureNumIterations

toString

getRevision

main