AdditiveRegression (weka-stable 3.6.8 API)

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

All Implemented Interfaces:: Serializable, Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

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

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)

 -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: 1.25 $
Author:: Mark Hall (mhall@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`m_NumIterationsPerformed` The number of successfully generated base classifiers.
`protected double`	`m_shrinkage` Shrinkage (Learning rate).
`protected boolean`	`m_SuitableData` whether we have suitable data or nor (if not, ZeroR model is used)
`protected ZeroR`	`m_zeroR` The model for the mean

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
m_Classifiers, m_NumIterations

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.Classifier
m_Debug

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)` Build the classifier on the supplied data
`double`	`classifyInstance(Instance inst)` Classify an instance.
`protected String`	`defaultClassifierString()` String describing default classifier.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure
`String[]`	`getOptions()` Gets the current settings of the Classifier.
`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.
`String`	`globalInfo()` Returns a string describing this attribute evaluator
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`double`	`measureNumIterations()` return the number of iterations (base classifiers) completed
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setShrinkage(double l)` Set the shrinkage parameter
`String`	`shrinkageTipText()` Returns the tip text for this property
`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, getClassifierSpec, setClassifier

Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

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

- Field Detail
  - m_shrinkage
```
protected double m_shrinkage
```
    Shrinkage (Learning rate). Default = no shrinkage.
  - m_NumIterationsPerformed
```
protected int m_NumIterationsPerformed
```
    The number of successfully generated base classifiers.
  - m_zeroR
```
protected ZeroR m_zeroR
```
    The model for the mean
  - m_SuitableData
```
protected boolean m_SuitableData
```
    whether we have suitable data or nor (if not, ZeroR model is used)
- 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 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
  - defaultClassifierString
```
protected String defaultClassifierString()
```
    String describing default classifier.
    
    Overrides:
    
    defaultClassifierString in class SingleClassifierEnhancer
    
    Returns:
    
    the default classifier classname
  - listOptions
```
public Enumeration 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(String[] options)
                throws 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)
```
```
 -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:
    
    Exception - if an option is not supported
  - getOptions
```
public 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 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
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the 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 Exception
```
    Build the classifier on the supplied data
    
    Overrides:
    
    buildClassifier in class IteratedSingleClassifierEnhancer
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    Exception - if the classifier could not be built successfully
  - classifyInstance
```
public double classifyInstance(Instance inst)
                        throws Exception
```
    Classify an instance.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    inst - the instance to predict
    
    Returns:
    
    a prediction for the instance
    
    Throws:
    
    Exception - if an error occurs
  - enumerateMeasures
```
public Enumeration 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(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:
    
    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 String toString()
```
    Returns textual description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(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.IteratedSingleClassifierEnhancer

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.Classifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

m_shrinkage

m_NumIterationsPerformed

m_zeroR

m_SuitableData

Constructor Detail

AdditiveRegression

AdditiveRegression

Method Detail

globalInfo

getTechnicalInformation

defaultClassifierString

listOptions

setOptions

getOptions

shrinkageTipText

setShrinkage

getShrinkage

getCapabilities

buildClassifier

classifyInstance

enumerateMeasures

getMeasure

measureNumIterations

toString

getRevision

main