public class OneR extends AbstractClassifier implements TechnicalInformationHandler, Sourcable
@article{Holte1993, author = {R.C. Holte}, journal = {Machine Learning}, pages = {63-91}, title = {Very simple classification rules perform well on most commonly used datasets}, volume = {11}, year = {1993} }Valid options are:
-B <minimum bucket size> The minimum number of objects in a bucket (default: 6).
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
Constructor and Description |
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OneR() |
Modifier and Type | Method and Description |
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void |
buildClassifier(Instances instances)
Generates the classifier.
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double |
classifyInstance(Instance inst)
Classifies a given instance.
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Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
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int |
getMinBucketSize()
Get the value of minBucketSize.
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java.lang.String[] |
getOptions()
Gets the current settings of the OneR classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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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.
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java.lang.String |
globalInfo()
Returns a string describing classifier
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options..
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static void |
main(java.lang.String[] argv)
Main method for testing this class
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java.lang.String |
minBucketSizeTipText()
Returns the tip text for this property
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weka.classifiers.rules.OneR.OneRRule |
newNominalRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this nominal attribute.
|
weka.classifiers.rules.OneR.OneRRule |
newNumericRule(Attribute attr,
Instances data,
int[] missingValueCounts)
Create a rule branching on this numeric attribute
|
weka.classifiers.rules.OneR.OneRRule |
newRule(Attribute attr,
Instances data)
Create a rule branching on this attribute.
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void |
setMinBucketSize(int v)
Set the value of minBucketSize.
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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java.lang.String |
toSource(java.lang.String className)
Returns a string that describes the classifier as source.
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java.lang.String |
toString()
Returns a description of the classifier
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batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
makeCopy
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public double classifyInstance(Instance inst) throws java.lang.Exception
classifyInstance
in interface Classifier
classifyInstance
in class AbstractClassifier
inst
- the instance to be classifiedjava.lang.Exception
- if an error occurred during the predictionpublic Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClassifier
Capabilities
public void buildClassifier(Instances instances) throws java.lang.Exception
buildClassifier
in interface Classifier
instances
- the instances to be used for building the classifierjava.lang.Exception
- if the classifier can't be built successfullypublic weka.classifiers.rules.OneR.OneRRule newRule(Attribute attr, Instances data) throws java.lang.Exception
attr
- the attribute to branch ondata
- the data to be used for creating the rulejava.lang.Exception
- if the rule can't be built successfullypublic weka.classifiers.rules.OneR.OneRRule newNominalRule(Attribute attr, Instances data, int[] missingValueCounts) throws java.lang.Exception
attr
- the attribute to branch ondata
- the data to be used for creating the rulemissingValueCounts
- to be filled injava.lang.Exception
- if the rule can't be built successfullypublic weka.classifiers.rules.OneR.OneRRule newNumericRule(Attribute attr, Instances data, int[] missingValueCounts) throws java.lang.Exception
attr
- the attribute to branch ondata
- the data to be used for creating the rulemissingValueCounts
- to be filled injava.lang.Exception
- if the rule can't be built successfullypublic java.util.Enumeration<Option> listOptions()
listOptions
in interface OptionHandler
listOptions
in class AbstractClassifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-B <minimum bucket size> The minimum number of objects in a bucket (default: 6).
setOptions
in interface OptionHandler
setOptions
in class AbstractClassifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class AbstractClassifier
public java.lang.String toSource(java.lang.String className) throws java.lang.Exception
public static double classify(Object[] i);
where the array i
contains elements that are either Double,
String, with missing values represented as null. The generated code is
public domain and comes with no warranty.public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String minBucketSizeTipText()
public int getMinBucketSize()
public void setMinBucketSize(int v)
v
- Value to assign to minBucketSize.public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier
public static void main(java.lang.String[] argv)
argv
- the commandline options