Package weka.classifiers.meta
Class RegressionByDiscretization
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.meta.RegressionByDiscretization
- All Implemented Interfaces:
Serializable,Cloneable,Classifier,ConditionalDensityEstimator,IntervalEstimator,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,OptionHandler,RevisionHandler
public class RegressionByDiscretization
extends SingleClassifierEnhancer
implements IntervalEstimator, ConditionalDensityEstimator
A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized. The predicted value is the expected value of the mean class value for each discretized interval (based on the predicted probabilities for each interval).
Valid options are:
-B <int> Number of bins for equal-width discretization (default 10).
-E Whether to delete empty bins after discretization (default false).
-F Use equal-frequency instead of equal-width discretization.
-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.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U Use unpruned tree.
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of instances> Set minimum number of instances per leaf. (default 2)
-R Use reduced error pruning.
-N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
-B Use binary splits only.
-S Don't perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-Q <seed> Seed for random data shuffling (default 1).
- Version:
- $Revision: 15520 $
- Author:
- Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
- See Also:
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Field Summary
Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoidbuildClassifier(Instances instances) Generates the classifier.doubleclassifyInstance(Instance instance) Returns a predicted class for the test instance.Returns the tip text for this propertyReturns the tip text for this propertyReturns default capabilities of the classifier.booleanGets whether empty bins are deleted.Get the estimatorbooleanGets whether to min.intGets the number of bins numeric attributes will be divided intoString[]Gets the current settings of the Classifier.Returns the revision string.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.booleanGet the value of UseEqualFrequency.Returns a string describing classifierReturns an enumeration describing the available options.doublelogDensity(Instance instance, double value) Returns natural logarithm of density estimate for given value based on given instance.static voidMain method for testing this class.Returns the tip text for this propertyReturns the tip text for this propertydouble[][]predictIntervals(Instance instance, double confidenceLevel) Returns an N * 2 array, where N is the number of prediction intervals.voidsetDeleteEmptyBins(boolean b) Sets whether to delete empty bins.voidsetEstimator(UnivariateDensityEstimator estimator) Set the estimatorvoidsetMinimizeAbsoluteError(boolean b) Sets whether to min.voidsetNumBins(int numBins) Sets the number of bins to divide each selected numeric attribute intovoidsetOptions(String[] options) Parses a given list of options.voidsetUseEqualFrequency(boolean newUseEqualFrequency) Set the value of UseEqualFrequency.toString()Returns a description of the classifier.Returns the tip text for this propertyMethods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifierMethods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
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Constructor Details
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RegressionByDiscretization
public RegressionByDiscretization()Default constructor.
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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.- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilitiesin interfaceCapabilitiesHandler- Specified by:
getCapabilitiesin interfaceClassifier- Overrides:
getCapabilitiesin classSingleClassifierEnhancer- Returns:
- the capabilities of this classifier
- See Also:
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buildClassifier
Generates the classifier.- Specified by:
buildClassifierin interfaceClassifier- Parameters:
instances- set of instances serving as training data- Throws:
Exception- if the classifier has not been generated successfully
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predictIntervals
Returns an N * 2 array, where N is the number of prediction intervals. In each row, the first element contains the lower boundary of the corresponding prediction interval and the second element the upper boundary.- Specified by:
predictIntervalsin interfaceIntervalEstimator- Parameters:
inst- the instance to make the prediction for.confidenceLevel- the percentage of cases that the interval should cover.- Returns:
- an array of prediction intervals
- Throws:
Exception- if the intervals can't be computed
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logDensity
Returns natural logarithm of density estimate for given value based on given instance.- Specified by:
logDensityin interfaceConditionalDensityEstimator- Parameters:
inst- the instance to make the prediction for.the- value to make the prediction for.- Returns:
- the natural logarithm of the density estimate
- Throws:
Exception- if the intervals can't be computed
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classifyInstance
Returns a predicted class for the test instance.- Specified by:
classifyInstancein interfaceClassifier- Overrides:
classifyInstancein classAbstractClassifier- Parameters:
instance- the instance to be classified- Returns:
- predicted class value
- Throws:
Exception- if the prediction couldn't be made
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listOptions
Returns an enumeration describing the available options.- Specified by:
listOptionsin interfaceOptionHandler- Overrides:
listOptionsin classSingleClassifierEnhancer- Returns:
- an enumeration of all the available options.
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setOptions
Parses a given list of options.- Specified by:
setOptionsin interfaceOptionHandler- Overrides:
setOptionsin classSingleClassifierEnhancer- Parameters:
options- the list of options as an array of strings- Throws:
Exception- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- Specified by:
getOptionsin interfaceOptionHandler- Overrides:
getOptionsin classSingleClassifierEnhancer- Returns:
- an array of strings suitable for passing to setOptions
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numBinsTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNumBins
public int getNumBins()Gets the number of bins numeric attributes will be divided into- Returns:
- the number of bins.
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setNumBins
public void setNumBins(int numBins) Sets the number of bins to divide each selected numeric attribute into- Parameters:
numBins- the number of bins
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deleteEmptyBinsTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getDeleteEmptyBins
public boolean getDeleteEmptyBins()Gets whether empty bins are deleted.- Returns:
- true if empty bins get deleted.
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setDeleteEmptyBins
public void setDeleteEmptyBins(boolean b) Sets whether to delete empty bins.- Parameters:
b- if true, empty bins will be deleted
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minimizeAbsoluteErrorTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getMinimizeAbsoluteError
public boolean getMinimizeAbsoluteError()Gets whether to min. abs. error- Returns:
- true if abs. err. is to be minimized
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setMinimizeAbsoluteError
public void setMinimizeAbsoluteError(boolean b) Sets whether to min. abs. error.- Parameters:
b- if true, abs. err. is minimized
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useEqualFrequencyTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getUseEqualFrequency
public boolean getUseEqualFrequency()Get the value of UseEqualFrequency.- Returns:
- Value of UseEqualFrequency.
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setUseEqualFrequency
public void setUseEqualFrequency(boolean newUseEqualFrequency) Set the value of UseEqualFrequency.- Parameters:
newUseEqualFrequency- Value to assign to UseEqualFrequency.
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estimatorTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getEstimator
Get the estimator- Returns:
- the estimator
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setEstimator
Set the estimator- Parameters:
newEstimator- the estimator to use
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toString
Returns a description of the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv- the options
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