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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.rules.PART
public class PART
Class for generating a PART decision list. Uses separate-and-conquer. Builds a partial C4.5 decision tree in each iteration and makes the "best" leaf into a rule.
For more information, see:
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
@inproceedings{Frank1998, author = {Eibe Frank and Ian H. Witten}, booktitle = {Fifteenth International Conference on Machine Learning}, editor = {J. Shavlik}, pages = {144-151}, publisher = {Morgan Kaufmann}, title = {Generating Accurate Rule Sets Without Global Optimization}, year = {1998}, PS = {http://www.cs.waikato.ac.nz/\~eibe/pubs/ML98-57.ps.gz} }Valid options are:
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of objects> Set minimum number of objects 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.
-U Generate unpruned decision list.
-Q <seed> Seed for random data shuffling (default 1).
Constructor Summary | |
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PART()
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Method Summary | |
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java.lang.String |
binarySplitsTipText()
Returns the tip text for this property |
void |
buildClassifier(Instances instances)
Generates the classifier. |
double |
classifyInstance(Instance instance)
Classifies an instance. |
java.lang.String |
confidenceFactorTipText()
Returns the tip text for this property |
double[] |
distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names |
boolean |
getBinarySplits()
Get the value of binarySplits. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
float |
getConfidenceFactor()
Get the value of CF. |
double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure |
int |
getMinNumObj()
Get the value of minNumObj. |
int |
getNumFolds()
Get the value of numFolds. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
boolean |
getReducedErrorPruning()
Get the value of reducedErrorPruning. |
int |
getSeed()
Get the value of Seed. |
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 |
getUnpruned()
Get the value of unpruned. |
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 |
measureNumRules()
Return the number of rules. |
java.lang.String |
minNumObjTipText()
Returns the tip text for this property |
java.lang.String |
numFoldsTipText()
Returns the tip text for this property |
java.lang.String |
reducedErrorPruningTipText()
Returns the tip text for this property |
java.lang.String |
seedTipText()
Returns the tip text for this property |
void |
setBinarySplits(boolean v)
Set the value of binarySplits. |
void |
setConfidenceFactor(float v)
Set the value of CF. |
void |
setMinNumObj(int v)
Set the value of minNumObj. |
void |
setNumFolds(int v)
Set the value of numFolds. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setReducedErrorPruning(boolean v)
Set the value of reducedErrorPruning. |
void |
setSeed(int newSeed)
Set the value of Seed. |
void |
setUnpruned(boolean newunpruned)
Set the value of unpruned. |
java.lang.String |
toString()
Returns a description of the classifier |
java.lang.String |
toSummaryString()
Returns a superconcise version of the model |
java.lang.String |
unprunedTipText()
Returns the tip text for this property |
Methods inherited from class weka.classifiers.Classifier |
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debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public PART()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public Capabilities getCapabilities()
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in class Classifier
instances
- the data to train with
java.lang.Exception
- if classifier can't be built successfullypublic double classifyInstance(Instance instance) throws java.lang.Exception
classifyInstance
in class Classifier
instance
- the instance to classify
java.lang.Exception
- if instance can't be classified successfullypublic final double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to get the distribution for
java.lang.Exception
- if the distribution can't be computed successfullypublic java.util.Enumeration listOptions()
-C confidence
Set confidence threshold for pruning. (Default: 0.25)
-M number
Set minimum number of instances per leaf. (Default: 2)
-R
Use reduced error pruning.
-N number
Set number of folds for reduced error pruning. One fold is
used as the pruning set. (Default: 3)
-B
Use binary splits for nominal attributes.
-U
Generate unpruned decision list.
-Q
The seed for reduced-error pruning.
listOptions
in interface OptionHandler
listOptions
in class Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of objects> Set minimum number of objects 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.
-U Generate unpruned decision list.
-Q <seed> Seed for random data shuffling (default 1).
setOptions
in interface OptionHandler
setOptions
in class Classifier
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 Classifier
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String toSummaryString()
toSummaryString
in interface Summarizable
public double measureNumRules()
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 value
java.lang.IllegalArgumentException
- if the named measure is not supportedpublic java.lang.String confidenceFactorTipText()
public float getConfidenceFactor()
public void setConfidenceFactor(float v)
v
- Value to assign to CF.public java.lang.String minNumObjTipText()
public int getMinNumObj()
public void setMinNumObj(int v)
v
- Value to assign to minNumObj.public java.lang.String reducedErrorPruningTipText()
public boolean getReducedErrorPruning()
public void setReducedErrorPruning(boolean v)
v
- Value to assign to reducedErrorPruning.public java.lang.String unprunedTipText()
public boolean getUnpruned()
public void setUnpruned(boolean newunpruned)
newunpruned
- Value to assign to unpruned.public java.lang.String numFoldsTipText()
public int getNumFolds()
public void setNumFolds(int v)
v
- Value to assign to numFolds.public java.lang.String seedTipText()
public int getSeed()
public void setSeed(int newSeed)
newSeed
- Value to assign to Seed.public java.lang.String binarySplitsTipText()
public boolean getBinarySplits()
public void setBinarySplits(boolean v)
v
- Value to assign to binarySplits.public static void main(java.lang.String[] argv)
argv
- command line options
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