public class RegressionByDiscretization extends SingleClassifierEnhancer implements IntervalEstimator, ConditionalDensityEstimator
-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).
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT| Constructor and Description |
|---|
RegressionByDiscretization()
Default constructor.
|
| Modifier and Type | Method and Description |
|---|---|
void |
buildClassifier(Instances instances)
Generates the classifier.
|
double |
classifyInstance(Instance instance)
Returns a predicted class for the test instance.
|
java.lang.String |
deleteEmptyBinsTipText()
Returns the tip text for this property
|
java.lang.String |
estimatorTipText()
Returns the tip text for this property
|
Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
|
boolean |
getDeleteEmptyBins()
Gets whether empty bins are deleted.
|
UnivariateDensityEstimator |
getEstimator()
Get the estimator
|
boolean |
getMinimizeAbsoluteError()
Gets whether to min.
|
int |
getNumBins()
Gets the number of bins numeric attributes will be divided into
|
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.
|
boolean |
getUseEqualFrequency()
Get the value of UseEqualFrequency.
|
java.lang.String |
globalInfo()
Returns a string describing classifier
|
java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
|
double |
logDensity(Instance instance,
double value)
Returns natural logarithm of density estimate for given value based on given instance.
|
static void |
main(java.lang.String[] argv)
Main method for testing this class.
|
java.lang.String |
minimizeAbsoluteErrorTipText()
Returns the tip text for this property
|
java.lang.String |
numBinsTipText()
Returns the tip text for this property
|
double[][] |
predictIntervals(Instance instance,
double confidenceLevel)
Returns an N * 2 array, where N is the number of prediction
intervals.
|
void |
setDeleteEmptyBins(boolean b)
Sets whether to delete empty bins.
|
void |
setEstimator(UnivariateDensityEstimator estimator)
Set the estimator
|
void |
setMinimizeAbsoluteError(boolean b)
Sets whether to min.
|
void |
setNumBins(int numBins)
Sets the number of bins to divide each selected numeric attribute into
|
void |
setOptions(java.lang.String[] options)
Parses a given list of options.
|
void |
setUseEqualFrequency(boolean newUseEqualFrequency)
Set the value of UseEqualFrequency.
|
java.lang.String |
toString()
Returns a description of the classifier.
|
java.lang.String |
useEqualFrequencyTipText()
Returns the tip text for this property
|
classifierTipText, getClassifier, postExecution, preExecution, setClassifierbatchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacespublic RegressionByDiscretization()
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
public Capabilities getCapabilities()
getCapabilities in interface ClassifiergetCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilitiespublic void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier in interface Classifierinstances - set of instances serving as training datajava.lang.Exception - if the classifier has not been generated successfullypublic double[][] predictIntervals(Instance instance, double confidenceLevel) throws java.lang.Exception
predictIntervals in interface IntervalEstimatorinst - the instance to make the prediction for.confidenceLevel - the percentage of cases that the interval should cover.java.lang.Exception - if the intervals can't be computedpublic double logDensity(Instance instance, double value) throws java.lang.Exception
logDensity in interface ConditionalDensityEstimatorinst - the instance to make the prediction for.the - value to make the prediction for.java.lang.Exception - if the intervals can't be computedpublic double classifyInstance(Instance instance) throws java.lang.Exception
classifyInstance in interface ClassifierclassifyInstance in class AbstractClassifierinstance - the instance to be classifiedjava.lang.Exception - if the prediction couldn't be madepublic java.util.Enumeration<Option> listOptions()
listOptions in interface OptionHandlerlistOptions in class SingleClassifierEnhancerpublic void setOptions(java.lang.String[] options)
throws java.lang.Exception
setOptions in interface OptionHandlersetOptions in class SingleClassifierEnhanceroptions - the list of options as an array of stringsjava.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class SingleClassifierEnhancerpublic java.lang.String numBinsTipText()
public int getNumBins()
public void setNumBins(int numBins)
numBins - the number of binspublic java.lang.String deleteEmptyBinsTipText()
public boolean getDeleteEmptyBins()
public void setDeleteEmptyBins(boolean b)
b - if true, empty bins will be deletedpublic java.lang.String minimizeAbsoluteErrorTipText()
public boolean getMinimizeAbsoluteError()
public void setMinimizeAbsoluteError(boolean b)
b - if true, abs. err. is minimizedpublic java.lang.String useEqualFrequencyTipText()
public boolean getUseEqualFrequency()
public void setUseEqualFrequency(boolean newUseEqualFrequency)
newUseEqualFrequency - Value to assign to UseEqualFrequency.public java.lang.String estimatorTipText()
public UnivariateDensityEstimator getEstimator()
public void setEstimator(UnivariateDensityEstimator estimator)
newEstimator - the estimator to usepublic java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class AbstractClassifierpublic static void main(java.lang.String[] argv)
argv - the options