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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.rules.OneR
public class OneR
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes. For more information, see:
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
@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).
| Constructor Summary | |
|---|---|
OneR()
|
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances instances)
Generates the classifier. |
double |
classifyInstance(Instance inst)
Classifies a given instance. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
int |
getMinBucketSize()
Get the value of minBucketSize. |
java.lang.String[] |
getOptions()
Gets the current settings of the OneR 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. |
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 |
java.lang.String |
minBucketSizeTipText()
Returns the tip text for this property |
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. |
void |
setMinBucketSize(int v)
Set the value of minBucketSize. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
java.lang.String |
toSource(java.lang.String className)
Returns a string that describes the classifier as source. |
java.lang.String |
toString()
Returns a description of the classifier |
| Methods inherited from class weka.classifiers.Classifier |
|---|
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public OneR()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandler
public double classifyInstance(Instance inst)
throws java.lang.Exception
classifyInstance in class Classifierinst - the instance to be classified
java.lang.Exception - if an error occurred during the predictionpublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilities
public void buildClassifier(Instances instances)
throws java.lang.Exception
buildClassifier in class Classifierinstances - the instances to be used for building the classifier
java.lang.Exception - if the classifier can't be built successfully
public 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 rule
java.lang.Exception - if the rule can't be built successfully
public 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 in
java.lang.Exception - if the rule can't be built successfully
public 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 in
java.lang.Exception - if the rule can't be built successfullypublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
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 OptionHandlersetOptions in class Classifieroptions - 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 OptionHandlergetOptions in class Classifier
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.
toSource in interface SourcableclassName - the name that should be given to the source class.
java.lang.Exception - if the souce can't be computedpublic java.lang.String toString()
toString in class java.lang.Objectpublic 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 RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the commandline options
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