Bagging (weka-dev 3.7.10 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.ParallelIteratedSingleClassifierEnhancer
      - weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
        
        weka.classifiers.meta.Bagging

All Implemented Interfaces:: Serializable, Cloneable, Classifier, AdditionalMeasureProducer, Aggregateable<Bagging>, CapabilitiesHandler, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class Bagging
extends RandomizableParallelIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler, PartitionGenerator, Aggregateable<Bagging>

Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner.

For more information, see

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

BibTeX:

 @article{Breiman1996,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {2},
    pages = {123-140},
    title = {Bagging predictors},
    volume = {24},
    year = {1996}
 }

Valid options are:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)

 -O
  Calculate the out of bag error.

 -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.REPTree)

 
 Options specific to classifier weka.classifiers.trees.REPTree:

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Version:: $Revision: 9785 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)
See Also:: Serialized Form

Constructor Summary

Constructors
Constructor and Description

Bagging()
Constructor.

Constructors
Constructor and Description
`Bagging()` Constructor.

Method Summary

Methods
Modifier and Type	Method and Description
`Bagging`	`aggregate(Bagging toAggregate)` Aggregate an object with this one
`String`	`bagSizePercentTipText()` Returns the tip text for this property
`void`	`buildClassifier(Instances data)` Bagging method.
`String`	`calcOutOfBagTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`void`	`generatePartition(Instances data)` Builds the classifier to generate a partition.
`int`	`getBagSizePercent()` Gets the size of each bag, as a percentage of the training set size.
`boolean`	`getCalcOutOfBag()` Get whether the out of bag error is calculated.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`double[]`	`getMembershipValues(Instance inst)` Computes an array that indicates leaf membership
`String[]`	`getOptions()` Gets the current settings of the Classifier.
`String`	`getRevision()` Returns the revision string.
`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 classifier
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`double`	`measureOutOfBagError()` Gets the out of bag error that was calculated as the classifier was built.
`int`	`numElements()` Returns the number of elements in the partition.
`void`	`setBagSizePercent(int newBagSizePercent)` Sets the size of each bag, as a percentage of the training set size.
`void`	`setCalcOutOfBag(boolean calcOutOfBag)` Set whether the out of bag error is calculated.
`void`	`setOptions(String[] options)` Parses a given list of options.
`String`	`toString()` Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer
getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

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

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

Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

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

Methods inherited from interface weka.core.CapabilitiesHandler
getCapabilities

- Constructor Detail
  - Bagging
```
public Bagging()
```
    Constructor.
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    a description 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 Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableParallelIteratedSingleClassifierEnhancer
    
    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:
```
 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
```
```
 -O
  Calculate the out of bag error.
```
```
 -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.REPTree)
```
```
 
 Options specific to classifier weka.classifiers.trees.REPTree:
 
```
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
```
```
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
```
```
 -N <number of folds>
  Number of folds for reduced error pruning (default 3).
```
```
 -S <seed>
  Seed for random data shuffling (default 1).
```
```
 -P
  No pruning.
```
```
 -L
  Maximum tree depth (default -1, no maximum)
```
    Options after -- are passed to the designated classifier.
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableParallelIteratedSingleClassifierEnhancer
    
    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 RandomizableParallelIteratedSingleClassifierEnhancer
    
    Returns:
    an array of strings suitable for passing to setOptions
  - bagSizePercentTipText
```
public String bagSizePercentTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBagSizePercent
```
public int getBagSizePercent()
```
    Gets the size of each bag, as a percentage of the training set size.
    
    Returns:
    the bag size, as a percentage.
  - setBagSizePercent
```
public void setBagSizePercent(int newBagSizePercent)
```
    Sets the size of each bag, as a percentage of the training set size.
    
    Parameters:
    newBagSizePercent - the bag size, as a percentage.
  - calcOutOfBagTipText
```
public String calcOutOfBagTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCalcOutOfBag
```
public void setCalcOutOfBag(boolean calcOutOfBag)
```
    Set whether the out of bag error is calculated.
    
    Parameters:
    calcOutOfBag - whether to calculate the out of bag error
  - getCalcOutOfBag
```
public boolean getCalcOutOfBag()
```
    Get whether the out of bag error is calculated.
    
    Returns:
    whether the out of bag error is calculated
  - measureOutOfBagError
```
public double measureOutOfBagError()
```
    Gets the out of bag error that was calculated as the classifier was built.
    
    Returns:
    the out of bag error
  - 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
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Bagging method.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Overrides:
    
    buildClassifier in class ParallelIteratedSingleClassifierEnhancer
    
    Parameters:
    data - the training data to be used for generating the bagged classifier.
    
    Throws:
    
    Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    instance - the instance to be classified
    
    Returns:
    preedicted class probability distribution
    
    Throws:
    
    Exception - if distribution can't be computed successfully
  - toString
```
public String toString()
```
    Returns description of the bagged classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    description of the bagged classifier as a string
  - generatePartition
```
public void generatePartition(Instances data)
                       throws Exception
```
    Builds the classifier to generate a partition.
    
    Specified by:
    
    generatePartition in interface PartitionGenerator
    
    Throws:
    
    Exception
  - getMembershipValues
```
public double[] getMembershipValues(Instance inst)
                             throws Exception
```
    Computes an array that indicates leaf membership
    
    Specified by:
    
    getMembershipValues in interface PartitionGenerator
    
    Throws:
    
    Exception
  - numElements
```
public int numElements()
                throws Exception
```
    Returns the number of elements in the partition.
    
    Specified by:
    
    numElements in interface PartitionGenerator
    
    Throws:
    
    Exception
  - getRevision
```
public 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(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    argv - the options
  - aggregate
```
public Bagging aggregate(Bagging toAggregate)
                  throws Exception
```
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<Bagging>
    
    Parameters:
    toAggregate - the object to aggregate
    
    Returns:
    the result of aggregation
    
    Throws:
    
    Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws Exception
```
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<Bagging>
    
    Throws:
    
    Exception - if the aggregation can't be finalized for some reason

Class Bagging

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

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.CapabilitiesHandler

Constructor Detail

Bagging

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

bagSizePercentTipText

getBagSizePercent

setBagSizePercent

calcOutOfBagTipText

setCalcOutOfBag

getCalcOutOfBag

measureOutOfBagError

enumerateMeasures

getMeasure

buildClassifier

distributionForInstance

toString

generatePartition

getMembershipValues

numElements

getRevision

main

aggregate

finalizeAggregation