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java.lang.Objectweka.classifiers.AbstractClassifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.meta.RegressionByDiscretization
public class RegressionByDiscretization
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).
Field Summary | |
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static int |
ESTIMATOR_HISTOGRAM
Use histogram estimator |
static int |
ESTIMATOR_KERNEL
filter: Standardize training data |
static int |
ESTIMATOR_NORMAL
filter: No normalization/standardization |
static Tag[] |
TAGS_ESTIMATOR
The filter to apply to the training data |
Constructor Summary | |
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RegressionByDiscretization()
Default constructor. |
Method Summary | |
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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 |
estimatorTypeTipText()
Returns the tip text for this property |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
boolean |
getDeleteEmptyBins()
Gets whether empty bins are deleted. |
SelectedTag |
getEstimatorType()
Get the estimator type |
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 |
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 |
setEstimatorType(SelectedTag newEstimator)
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 |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
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classifierTipText, getClassifier, setClassifier |
Methods inherited from class weka.classifiers.AbstractClassifier |
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debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Field Detail |
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public static final int ESTIMATOR_HISTOGRAM
public static final int ESTIMATOR_KERNEL
public static final int ESTIMATOR_NORMAL
public static final Tag[] TAGS_ESTIMATOR
Constructor Detail |
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public RegressionByDiscretization()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
public Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class SingleClassifierEnhancer
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in interface Classifier
instances
- set of instances serving as training data
java.lang.Exception
- if the classifier has not been generated successfullypublic double[][] predictIntervals(Instance instance, double confidenceLevel) throws java.lang.Exception
predictIntervals
in interface IntervalEstimator
inst
- 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 ConditionalDensityEstimator
inst
- 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 Classifier
classifyInstance
in class AbstractClassifier
instance
- the instance to be classified
java.lang.Exception
- if the prediction couldn't be madepublic java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class SingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
setOptions
in interface OptionHandler
setOptions
in class SingleClassifierEnhancer
options
- 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 OptionHandler
getOptions
in class SingleClassifierEnhancer
public 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 estimatorTypeTipText()
public SelectedTag getEstimatorType()
public void setEstimatorType(SelectedTag newEstimator)
newEstimator
- the estimator to usepublic java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
public static void main(java.lang.String[] argv)
argv
- the options
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