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Class weka.classifiers.AdditiveRegression

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.AdditiveRegression

public class AdditiveRegression
extends Classifier
implements OptionHandler, AdditionalMeasureProducer
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. Smoothing is accomplished through varying the shrinkage (learning rate) parameter.

 Analysing:  Root_relative_squared_error
 Datasets:   36
 Resultsets: 2
 Confidence: 0.05 (two tailed)
 Date:       10/13/00 10:00 AM
 Dataset                   (1) m5.M5Prim | (2) AdditiveRegression -S 0.7 \
                                         |    -B weka.classifiers.m5.M5Prime 
                          ----------------------------
 auto93.names              (10)    54.4  |    49.41 * 
 autoHorse.names           (10)    32.76 |    26.34 * 
 autoMpg.names             (10)    35.32 |    34.84 * 
 autoPrice.names           (10)    40.01 |    36.57 * 
 baskball                  (10)    79.46 |    79.85   
 bodyfat.names             (10)    10.38 |    11.41 v 
 bolts                     (10)    19.29 |    12.61 * 
 breastTumor               (10)    96.95 |    96.23 * 
 cholesterol               (10)   101.03 |    98.88 * 
 cleveland                 (10)    71.29 |    70.87 * 
 cloud                     (10)    38.82 |    39.18   
 cpu                       (10)    22.26 |    14.74 * 
 detroit                   (10)   228.16 |    83.7  * 
 echoMonths                (10)    71.52 |    69.15 * 
 elusage                   (10)    48.94 |    49.03   
 fishcatch                 (10)    16.61 |    15.36 * 
 fruitfly                  (10)   100    |   100    * 
 gascons                   (10)    18.72 |    14.26 * 
 housing                   (10)    38.62 |    36.53 * 
 hungarian                 (10)    74.67 |    72.19 * 
 longley                   (10)    31.23 |    28.26 * 
 lowbwt                    (10)    62.26 |    61.48 * 
 mbagrade                  (10)    89.2  |    89.2    
 meta                      (10)   163.15 |   188.28 v 
 pbc                       (10)    81.35 |    79.4  * 
 pharynx                   (10)   105.41 |   105.03   
 pollution                 (10)    72.24 |    68.16 * 
 pwLinear                  (10)    32.42 |    33.33 v 
 quake                     (10)   100.21 |    99.93   
 schlvote                  (10)    92.41 |    98.23 v 
 sensory                   (10)    88.03 |    87.94   
 servo                     (10)    37.07 |    35.5  * 
 sleep                     (10)    70.17 |    71.65   
 strike                    (10)    84.98 |    83.96 * 
 veteran                   (10)    90.61 |    88.77 * 
 vineyard                  (10)    79.41 |    73.95 * 
                        ----------------------------
                              (v| |*) |   (4|8|24) 
 

For more information see:

Friedman, J.H. (1999). Stochastic Gradient Boosting. Technical Report Stanford University. http://www-stat.stanford.edu/~jhf/ftp/stobst.ps.

Valid options from the command line are:

-B classifierstring
Classifierstring should contain the full class name of a classifier followed by options to the classifier. (required).

-S shrinkage rate
Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). (default = 1.0, ie no shrinkage).

-M max models
Set the maximum number of models to generate. Values <= 0 indicate no maximum, ie keep going until the reduction in error threshold is reached. (default = -1).

-D
Debugging output.

Author:
Mark Hall (mhall@cs.waikato.ac.nz)

Constructor Index

 o AdditiveRegression()
Default constructor specifying DecisionStump as the classifier
 o AdditiveRegression(Classifier)
Constructor which takes base classifier as argument.

Method Index

 o buildClassifier(Instances)
Build the classifier on the supplied data
 o classifierTipText()
Returns the tip text for this property
 o classifyInstance(Instance)
Classify an instance.
 o debugTipText()
Returns the tip text for this property
 o enumerateMeasures()
Returns an enumeration of the additional measure names
 o getClassifier()
Gets the classifier used.
 o getDebug()
Gets whether debugging has been turned on
 o getMaxModels()
Get the max number of models to generate
 o getMeasure(String)
Returns the value of the named measure
 o getOptions()
Gets the current settings of the Classifier.
 o getShrinkage()
Get the shrinkage rate.
 o globalInfo()
Returns a string describing this attribute evaluator
 o listOptions()
Returns an enumeration describing the available options
 o main(String[])
Main method for testing this class.
 o maxModelsTipText()
Returns the tip text for this property
 o measureNumIterations()
return the number of iterations (base classifiers) completed
 o setClassifier(Classifier)
Sets the classifier
 o setDebug(boolean)
Set whether debugging output is produced.
 o setMaxModels(int)
Set the maximum number of models to generate
 o setOptions(String[])
Parses a given list of options.
 o setShrinkage(double)
Set the shrinkage parameter
 o shrinkageTipText()
Returns the tip text for this property
 o toString()
Returns textual description of the classifier.

Constructors

 o AdditiveRegression
 public AdditiveRegression()
Default constructor specifying DecisionStump as the classifier

 o AdditiveRegression
 public AdditiveRegression(Classifier classifier)
Constructor which takes base classifier as argument.

Parameters:
classifier - the base classifier to use

Methods

 o 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
 o listOptions
 public Enumeration listOptions()
Returns an enumeration describing the available options

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

-B classifierstring
Classifierstring should contain the full class name of a classifier followed by options to the classifier. (required).

-S shrinkage rate
Smaller values help prevent overfitting and have a smoothing effect (but increase learning time). (default = 1.0, ie. no shrinkage).

-D
Debugging output.

-M max models
Set the maximum number of models to generate. Values <= 0 indicate no maximum, ie keep going until the reduction in error threshold is reached. (default = -1).

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
 o getOptions
 public String[] getOptions()
Gets the current settings of the Classifier.

Returns:
an array of strings suitable for passing to setOptions
 o debugTipText
 public String debugTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setDebug
 public void setDebug(boolean d)
Set whether debugging output is produced.

Parameters:
d - true if debugging output is to be produced
 o getDebug
 public boolean getDebug()
Gets whether debugging has been turned on

Returns:
true if debugging has been turned on
 o classifierTipText
 public String classifierTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setClassifier
 public void setClassifier(Classifier classifier)
Sets the classifier

Parameters:
classifier - the classifier with all options set.
 o getClassifier
 public Classifier getClassifier()
Gets the classifier used.

Returns:
the classifier
 o maxModelsTipText
 public String maxModelsTipText()
Returns the tip text for this property

Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o setMaxModels
 public void setMaxModels(int maxM)
Set the maximum number of models to generate

Parameters:
maxM - the maximum number of models
 o getMaxModels
 public int getMaxModels()
Get the max number of models to generate

Returns:
the max number of models to generate
 o 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
 o setShrinkage
 public void setShrinkage(double l)
Set the shrinkage parameter

Parameters:
l - the shrinkage rate.
 o getShrinkage
 public double getShrinkage()
Get the shrinkage rate.

Returns:
the value of the learning rate
 o buildClassifier
 public void buildClassifier(Instances data) throws Exception
Build the classifier on the supplied data

Parameters:
data - the training data
Throws: Exception
if the classifier could not be built successfully
Overrides:
buildClassifier in class Classifier
 o classifyInstance
 public double classifyInstance(Instance inst) throws Exception
Classify an instance.

Parameters:
inst - the instance to predict
Returns:
a prediction for the instance
Throws: Exception
if an error occurs
Overrides:
classifyInstance in class Classifier
 o enumerateMeasures
 public Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names

Returns:
an enumeration of the measure names
 o getMeasure
 public double getMeasure(String additionalMeasureName)
Returns the value of the named measure

Parameters:
measureName - 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
 o measureNumIterations
 public double measureNumIterations()
return the number of iterations (base classifiers) completed

Returns:
the number of iterations (same as number of base classifier models)
 o toString
 public String toString()
Returns textual description of the classifier.

Returns:
a description of the classifier as a string
Overrides:
toString in class Object
 o 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]

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