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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 | |
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M5Base()
Constructor |
Method Summary | |
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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()
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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 |
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debugTipText, distributionForInstance, forName, getDebug, getRevision, 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 M5Base()
Method Detail |
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public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
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 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 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 CapabilitiesHandler
getCapabilities
in class Classifier
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class Classifier
data
- set of instances serving as training data
java.lang.Exception
- if the classifier has not been generated
successfullypublic double classifyInstance(Instance inst) throws java.lang.Exception
classifyInstance
in class Classifier
inst
- 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.Object
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.Exception
- if the named measure is not supportedpublic double measureNumRules()
public RuleNode getM5RootNode()
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