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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
weka.classifiers.meta.Bagging
public class 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.
@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 Summary | |
|---|---|
Bagging()
Constructor. |
|
| Method Summary | |
|---|---|
java.lang.String |
bagSizePercentTipText()
Returns the tip text for this property |
void |
buildClassifier(Instances data)
Bagging method. |
java.lang.String |
calcOutOfBagTipText()
Returns the tip text for this property |
double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names. |
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(java.lang.String additionalMeasureName)
Returns the value of the named measure. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.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. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
double |
measureOutOfBagError()
Gets the out of bag error that was calculated as the classifier was built. |
Instances |
resampleWithWeights(Instances data,
java.util.Random random,
boolean[] sampled)
Creates a new dataset of the same size using random sampling with replacement according to the given weight vector. |
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(java.lang.String[] options)
Parses a given list of options. |
java.lang.String |
toString()
Returns description of the bagged classifier. |
| Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer |
|---|
getSeed, seedTipText, setSeed |
| 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.Classifier |
|---|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public Bagging()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions 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 OptionHandlersetOptions in class RandomizableIteratedSingleClassifierEnhanceroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class RandomizableIteratedSingleClassifierEnhancerpublic 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 AdditionalMeasureProducerpublic double getMeasure(java.lang.String additionalMeasureName)
getMeasure in interface AdditionalMeasureProduceradditionalMeasureName - the name of the measure to query for its value
java.lang.IllegalArgumentException - if the named measure is not supported
public final Instances resampleWithWeights(Instances data,
java.util.Random random,
boolean[] sampled)
data - the data to be sampled fromrandom - a random number generatorsampled - indicating which instance has been sampled
java.lang.IllegalArgumentException - if the weights array is of the wrong
length or contains negative weights.
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class IteratedSingleClassifierEnhancerdata - the training data to be used for generating the
bagged classifier.
java.lang.Exception - if the classifier could not be built successfully
public double[] distributionForInstance(Instance instance)
throws java.lang.Exception
distributionForInstance in class Classifierinstance - the instance to be classified
java.lang.Exception - if distribution can't be computed successfullypublic java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the options
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