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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.trees.m5.M5Base
public abstract class M5Base
M5Base. Implements base routines for generating M5 Model trees and rules.
The original algorithm M5 was invented by Quinlan:
Quinlan J. R. (1992). Learning with continuous classes. Proceedings of
the Australian Joint Conference on Artificial Intelligence. 343--348.
World Scientific, Singapore.
-U
Use unsmoothed predictions.
-R
Build regression tree/rule rather than model tree/rule
| Constructor Summary | |
|---|---|
M5Base()
Constructor |
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances data)
Generates the classifier. |
java.lang.String |
buildRegressionTreeTipText()
Returns the tip text for this property |
double |
classifyInstance(Instance inst)
Calculates a prediction for an instance using a set of rules or an M5 model tree |
java.util.Enumeration |
enumerateMeasures()
Returns an enumeration of the additional measure names |
java.lang.String |
generateRulesTipText()
Returns the tip text for this property |
boolean |
getBuildRegressionTree()
Get the value of regressionTree. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier, i.e., of LinearRegression. |
RuleNode |
getM5RootNode()
|
double |
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure |
double |
getMinNumInstances()
Get the minimum number of instances to allow at a leaf node |
java.lang.String[] |
getOptions()
Gets the current settings of the classifier. |
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 whether unpruned tree/rules are being generated |
boolean |
getUseUnsmoothed()
Get whether or not smoothing is being used |
java.lang.String |
globalInfo()
returns information about the classifier |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options |
double |
measureNumRules()
return the number of rules |
java.lang.String |
minNumInstancesTipText()
Returns the tip text for this property |
void |
setBuildRegressionTree(boolean newregressionTree)
Set the value of regressionTree. |
void |
setMinNumInstances(double minNum)
Set the minimum number of instances to allow at a leaf node |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setUnpruned(boolean unpruned)
Use unpruned tree/rules |
void |
setUseUnsmoothed(boolean s)
Use unsmoothed predictions |
java.lang.String |
toString()
Returns a description of the classifier |
java.lang.String |
unprunedTipText()
Returns the tip text for this property |
java.lang.String |
useUnsmoothedTipText()
Returns the tip text for this property |
| Methods inherited from class weka.classifiers.Classifier |
|---|
debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public M5Base()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-U
Use unsmoothed predictions.
-R
Build a regression tree rather than a model tree.
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 Classifierpublic java.lang.String unprunedTipText()
public void setUnpruned(boolean unpruned)
unpruned - true if unpruned tree/rules are to be generatedpublic boolean getUnpruned()
public java.lang.String generateRulesTipText()
public java.lang.String useUnsmoothedTipText()
public void setUseUnsmoothed(boolean s)
s - true if unsmoothed predictions are to be usedpublic boolean getUseUnsmoothed()
public java.lang.String buildRegressionTreeTipText()
public boolean getBuildRegressionTree()
public void setBuildRegressionTree(boolean newregressionTree)
newregressionTree - Value to assign to regressionTree.public java.lang.String minNumInstancesTipText()
public void setMinNumInstances(double minNum)
minNum - the minimum number of instancespublic double getMinNumInstances()
double valuepublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilities
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class Classifierdata - set of instances serving as training data
java.lang.Exception - if the classifier has not been generated
successfully
public double classifyInstance(Instance inst)
throws java.lang.Exception
classifyInstance in class Classifierinst - the instance whos class value is to be predicted
java.lang.Exception - if a prediction can't be made.public java.lang.String toString()
toString in class java.lang.Objectpublic java.util.Enumeration enumerateMeasures()
enumerateMeasures in interface AdditionalMeasureProducerpublic double getMeasure(java.lang.String additionalMeasureName)
getMeasure in interface AdditionalMeasureProduceradditionalMeasureName - the name of the measure to query for its value
java.lang.Exception - if the named measure is not supportedpublic double measureNumRules()
public RuleNode getM5RootNode()
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