public class Bagging extends RandomizableIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
@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.
Constructor and Description |
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Bagging()
Constructor.
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Modifier and Type | Method and Description |
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java.lang.String |
bagSizePercentTipText()
Returns the tip text for this property
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void |
buildClassifier(Instances data)
Bagging method.
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java.lang.String |
calcOutOfBagTipText()
Returns the tip text for this property
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double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.
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java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names.
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int |
getBagSizePercent()
Gets the size of each bag, as a percentage of the training set size.
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boolean |
getCalcOutOfBag()
Get whether the out of bag error is calculated.
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double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure.
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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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.
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java.lang.String |
globalInfo()
Returns a string describing classifier
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java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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double |
measureOutOfBagError()
Gets the out of bag error that was calculated as the classifier was built.
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void |
setBagSizePercent(int newBagSizePercent)
Sets the size of each bag, as a percentage of the training set size.
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void |
setCalcOutOfBag(boolean calcOutOfBag)
Set whether the out of bag error is calculated.
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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java.lang.String |
toString()
Returns description of the bagged classifier.
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getSeed, seedTipText, setSeed
getNumIterations, numIterationsTipText, setNumIterations
classifierTipText, getCapabilities, getClassifier, setClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-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.
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public java.lang.String bagSizePercentTipText()
public int getBagSizePercent()
public void setBagSizePercent(int newBagSizePercent)
newBagSizePercent
- the bag size, as a percentage.public java.lang.String calcOutOfBagTipText()
public void setCalcOutOfBag(boolean calcOutOfBag)
calcOutOfBag
- whether to calculate the out of bag errorpublic boolean getCalcOutOfBag()
public double measureOutOfBagError()
public java.util.Enumeration enumerateMeasures()
enumerateMeasures
in interface AdditionalMeasureProducer
public double getMeasure(java.lang.String additionalMeasureName)
getMeasure
in interface AdditionalMeasureProducer
additionalMeasureName
- the name of the measure to query for its valuejava.lang.IllegalArgumentException
- if the named measure is not supportedpublic void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the bagged
classifier.java.lang.Exception
- if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if distribution can't be computed successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class Classifier
public static void main(java.lang.String[] argv)
argv
- the options