Package weka.classifiers.trees
Class LMT
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.trees.LMT
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,AdditionalMeasureProducer
,CapabilitiesHandler
,Drawable
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class LMT extends Classifier implements OptionHandler, AdditionalMeasureProducer, Drawable, TechnicalInformationHandler
Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.
For more information see:
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205.
Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005. BibTeX:@article{Landwehr2005, author = {Niels Landwehr and Mark Hall and Eibe Frank}, journal = {Machine Learning}, number = {1-2}, pages = {161-205}, title = {Logistic Model Trees}, volume = {95}, year = {2005} } @inproceedings{Sumner2005, author = {Marc Sumner and Eibe Frank and Mark Hall}, booktitle = {9th European Conference on Principles and Practice of Knowledge Discovery in Databases}, pages = {675-683}, publisher = {Springer}, title = {Speeding up Logistic Model Tree Induction}, year = {2005} }
Valid options are:-B Binary splits (convert nominal attributes to binary ones)
-R Split on residuals instead of class values
-C Use cross-validation for boosting at all nodes (i.e., disable heuristic)
-P Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
-I <numIterations> Set fixed number of iterations for LogitBoost (instead of using cross-validation)
-M <numInstances> Set minimum number of instances at which a node can be split (default 15)
-W <beta> Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
-A The AIC is used to choose the best iteration.
- Version:
- $Revision: 5535 $
- Author:
- Niels Landwehr, Marc Sumner
- See Also:
- Serialized Form
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Field Summary
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Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE
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Constructor Summary
Constructors Constructor Description LMT()
Creates an instance of LMT with standard options
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
Builds the classifier.double
classifyInstance(Instance instance)
Classifies an instance.java.lang.String
convertNominalTipText()
Returns the tip text for this propertydouble[]
distributionForInstance(Instance instance)
Returns class probabilities for an instance.java.util.Enumeration
enumerateMeasures()
Returns an enumeration of the additional measure namesjava.lang.String
errorOnProbabilitiesTipText()
Returns the tip text for this propertyjava.lang.String
fastRegressionTipText()
Returns the tip text for this propertyCapabilities
getCapabilities()
Returns default capabilities of the classifier.boolean
getConvertNominal()
Get the value of convertNominal.boolean
getErrorOnProbabilities()
Get the value of errorOnProbabilities.boolean
getFastRegression()
Get the value of fastRegression.double
getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measureint
getMinNumInstances()
Get the value of minNumInstances.int
getNumBoostingIterations()
Get the value of numBoostingIterations.java.lang.String[]
getOptions()
Gets the current settings of the Classifier.java.lang.String
getRevision()
Returns the revision string.boolean
getSplitOnResiduals()
Get the value of splitOnResiduals.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
getUseAIC()
Get the value of useAIC.double
getWeightTrimBeta()
Get the value of weightTrimBeta.java.lang.String
globalInfo()
Returns a string describing classifierjava.lang.String
graph()
Returns graph describing the tree.int
graphType()
Returns the type of graph this classifier represents.java.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this classint
measureNumLeaves()
Returns the number of leaves in the treeint
measureTreeSize()
Returns the size of the treejava.lang.String
minNumInstancesTipText()
Returns the tip text for this propertyjava.lang.String
numBoostingIterationsTipText()
Returns the tip text for this propertyvoid
setConvertNominal(boolean c)
Set the value of convertNominal.void
setErrorOnProbabilities(boolean c)
Set the value of errorOnProbabilities.void
setFastRegression(boolean c)
Set the value of fastRegression.void
setMinNumInstances(int c)
Set the value of minNumInstances.void
setNumBoostingIterations(int c)
Set the value of numBoostingIterations.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setSplitOnResiduals(boolean c)
Set the value of splitOnResiduals.void
setUseAIC(boolean c)
Set the value of useAIC.void
setWeightTrimBeta(double n)
Set the value of weightTrimBeta.java.lang.String
splitOnResidualsTipText()
Returns the tip text for this propertyjava.lang.String
toString()
Returns a description of the classifier.java.lang.String
useAICTipText()
Returns the tip text for this propertyjava.lang.String
weightTrimBetaTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.Classifier
debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Builds the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
data
- the data to train with- Throws:
java.lang.Exception
- if classifier can't be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Returns class probabilities for an instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to compute the distribution for- Returns:
- the class probabilities
- Throws:
java.lang.Exception
- if distribution can't be computed successfully
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classifyInstance
public double classifyInstance(Instance instance) throws java.lang.Exception
Classifies an instance.- Overrides:
classifyInstance
in classClassifier
- Parameters:
instance
- the instance to classify- Returns:
- the classification
- Throws:
java.lang.Exception
- if instance can't be classified successfully
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toString
public java.lang.String toString()
Returns a description of the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string representation of the classifier
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-B Binary splits (convert nominal attributes to binary ones)
-R Split on residuals instead of class values
-C Use cross-validation for boosting at all nodes (i.e., disable heuristic)
-P Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
-I <numIterations> Set fixed number of iterations for LogitBoost (instead of using cross-validation)
-M <numInstances> Set minimum number of instances at which a node can be split (default 15)
-W <beta> Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
-A The AIC is used to choose the best iteration.
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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getWeightTrimBeta
public double getWeightTrimBeta()
Get the value of weightTrimBeta.
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getUseAIC
public boolean getUseAIC()
Get the value of useAIC.- Returns:
- Value of useAIC.
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setWeightTrimBeta
public void setWeightTrimBeta(double n)
Set the value of weightTrimBeta.
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setUseAIC
public void setUseAIC(boolean c)
Set the value of useAIC.- Parameters:
c
- Value to assign to useAIC.
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getConvertNominal
public boolean getConvertNominal()
Get the value of convertNominal.- Returns:
- Value of convertNominal.
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getSplitOnResiduals
public boolean getSplitOnResiduals()
Get the value of splitOnResiduals.- Returns:
- Value of splitOnResiduals.
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getFastRegression
public boolean getFastRegression()
Get the value of fastRegression.- Returns:
- Value of fastRegression.
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getErrorOnProbabilities
public boolean getErrorOnProbabilities()
Get the value of errorOnProbabilities.- Returns:
- Value of errorOnProbabilities.
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getNumBoostingIterations
public int getNumBoostingIterations()
Get the value of numBoostingIterations.- Returns:
- Value of numBoostingIterations.
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getMinNumInstances
public int getMinNumInstances()
Get the value of minNumInstances.- Returns:
- Value of minNumInstances.
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setConvertNominal
public void setConvertNominal(boolean c)
Set the value of convertNominal.- Parameters:
c
- Value to assign to convertNominal.
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setSplitOnResiduals
public void setSplitOnResiduals(boolean c)
Set the value of splitOnResiduals.- Parameters:
c
- Value to assign to splitOnResiduals.
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setFastRegression
public void setFastRegression(boolean c)
Set the value of fastRegression.- Parameters:
c
- Value to assign to fastRegression.
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setErrorOnProbabilities
public void setErrorOnProbabilities(boolean c)
Set the value of errorOnProbabilities.- Parameters:
c
- Value to assign to errorOnProbabilities.
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setNumBoostingIterations
public void setNumBoostingIterations(int c)
Set the value of numBoostingIterations.- Parameters:
c
- Value to assign to numBoostingIterations.
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setMinNumInstances
public void setMinNumInstances(int c)
Set the value of minNumInstances.- Parameters:
c
- Value to assign to minNumInstances.
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graphType
public int graphType()
Returns the type of graph this classifier represents.
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graph
public java.lang.String graph() throws java.lang.Exception
Returns graph describing the tree.
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measureTreeSize
public int measureTreeSize()
Returns the size of the tree- Returns:
- the size of the tree
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measureNumLeaves
public int measureNumLeaves()
Returns the number of leaves in the tree- Returns:
- the number of leaves in the tree
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enumerateMeasures
public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Returns:
- an enumeration of the measure names
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getMeasure
public double getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- Parameters:
additionalMeasureName
- the name of the measure to query for its value- Returns:
- the value of the named measure
- Throws:
java.lang.IllegalArgumentException
- if the named measure is not supported
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public 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.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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convertNominalTipText
public java.lang.String convertNominalTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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splitOnResidualsTipText
public java.lang.String splitOnResidualsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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fastRegressionTipText
public java.lang.String fastRegressionTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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errorOnProbabilitiesTipText
public java.lang.String errorOnProbabilitiesTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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numBoostingIterationsTipText
public java.lang.String numBoostingIterationsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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minNumInstancesTipText
public java.lang.String minNumInstancesTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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weightTrimBetaTipText
public java.lang.String weightTrimBetaTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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useAICTipText
public java.lang.String useAICTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class- Parameters:
argv
- the commandline options
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