LMT

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.trees.LMT

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

Field Summary
- Fields inherited from interface weka.core.Drawable
  BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

LMT()
Creates an instance of LMT with standard options

Constructors
Constructor and Description
`LMT()` Creates an instance of LMT with standard options

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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 property
`double[]`	`distributionForInstance(Instance instance)` Returns class probabilities for an instance.
`java.util.Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`errorOnProbabilitiesTipText()` Returns the tip text for this property
`java.lang.String`	`fastRegressionTipText()` Returns the tip text for this property
`Capabilities`	`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 measure
`int`	`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 classifier
`java.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 class
`int`	`measureNumLeaves()` Returns the number of leaves in the tree
`int`	`measureTreeSize()` Returns the size of the tree
`java.lang.String`	`minNumInstancesTipText()` Returns the tip text for this property
`java.lang.String`	`numBoostingIterationsTipText()` Returns the tip text for this property
`void`	`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 property
`java.lang.String`	`toString()` Returns a description of the classifier.
`java.lang.String`	`useAICTipText()` Returns the tip text for this property
`java.lang.String`	`weightTrimBetaTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier
debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - LMT
```
public LMT()
```
    Creates an instance of LMT with standard options
- Method Detail
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Builds the classifier.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Returns class probabilities for an instance.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance to compute the distribution for
    
    Returns:
    
    the class probabilities
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed successfully
  - classifyInstance
```
public double classifyInstance(Instance instance)
                        throws java.lang.Exception
```
    Classifies an instance.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    instance - the instance to classify
    
    Returns:
    
    the classification
    
    Throws:
    
    java.lang.Exception - if instance can't be classified successfully
  - toString
```
public java.lang.String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all the available options.
  - 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 interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getWeightTrimBeta
```
public double getWeightTrimBeta()
```
    Get the value of weightTrimBeta.
  - getUseAIC
```
public boolean getUseAIC()
```
    Get the value of useAIC.
    
    Returns:
    
    Value of useAIC.
  - setWeightTrimBeta
```
public void setWeightTrimBeta(double n)
```
    Set the value of weightTrimBeta.
  - setUseAIC
```
public void setUseAIC(boolean c)
```
    Set the value of useAIC.
    
    Parameters:
    
    c - Value to assign to useAIC.
  - getConvertNominal
```
public boolean getConvertNominal()
```
    Get the value of convertNominal.
    
    Returns:
    
    Value of convertNominal.
  - getSplitOnResiduals
```
public boolean getSplitOnResiduals()
```
    Get the value of splitOnResiduals.
    
    Returns:
    
    Value of splitOnResiduals.
  - getFastRegression
```
public boolean getFastRegression()
```
    Get the value of fastRegression.
    
    Returns:
    
    Value of fastRegression.
  - getErrorOnProbabilities
```
public boolean getErrorOnProbabilities()
```
    Get the value of errorOnProbabilities.
    
    Returns:
    
    Value of errorOnProbabilities.
  - getNumBoostingIterations
```
public int getNumBoostingIterations()
```
    Get the value of numBoostingIterations.
    
    Returns:
    
    Value of numBoostingIterations.
  - getMinNumInstances
```
public int getMinNumInstances()
```
    Get the value of minNumInstances.
    
    Returns:
    
    Value of minNumInstances.
  - setConvertNominal
```
public void setConvertNominal(boolean c)
```
    Set the value of convertNominal.
    
    Parameters:
    
    c - Value to assign to convertNominal.
  - setSplitOnResiduals
```
public void setSplitOnResiduals(boolean c)
```
    Set the value of splitOnResiduals.
    
    Parameters:
    
    c - Value to assign to splitOnResiduals.
  - setFastRegression
```
public void setFastRegression(boolean c)
```
    Set the value of fastRegression.
    
    Parameters:
    
    c - Value to assign to fastRegression.
  - setErrorOnProbabilities
```
public void setErrorOnProbabilities(boolean c)
```
    Set the value of errorOnProbabilities.
    
    Parameters:
    
    c - Value to assign to errorOnProbabilities.
  - setNumBoostingIterations
```
public void setNumBoostingIterations(int c)
```
    Set the value of numBoostingIterations.
    
    Parameters:
    
    c - Value to assign to numBoostingIterations.
  - setMinNumInstances
```
public void setMinNumInstances(int c)
```
    Set the value of minNumInstances.
    
    Parameters:
    
    c - Value to assign to minNumInstances.
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    Drawable.TREE
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph describing the tree
    
    Throws:
    
    java.lang.Exception - if graph can't be computed
  - measureTreeSize
```
public int measureTreeSize()
```
    Returns the size of the tree
    
    Returns:
    
    the size of the tree
  - measureNumLeaves
```
public int measureNumLeaves()
```
    Returns the number of leaves in the tree
    
    Returns:
    
    the number of leaves in the tree
  - enumerateMeasures
```
public java.util.Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    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
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - 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 interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class
    
    Parameters:
    
    argv - the commandline options

Class LMT

Field Summary

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

LMT

Method Detail

getCapabilities

buildClassifier

distributionForInstance

classifyInstance

toString

listOptions

setOptions

getOptions

getWeightTrimBeta

getUseAIC

setWeightTrimBeta

setUseAIC

getConvertNominal

getSplitOnResiduals

getFastRegression

getErrorOnProbabilities

getNumBoostingIterations

getMinNumInstances

setConvertNominal

setSplitOnResiduals

setFastRegression

setErrorOnProbabilities

setNumBoostingIterations

setMinNumInstances

graphType

graph

measureTreeSize

measureNumLeaves

enumerateMeasures

getMeasure

globalInfo

getTechnicalInformation

convertNominalTipText

splitOnResidualsTipText

fastRegressionTipText

errorOnProbabilitiesTipText

numBoostingIterationsTipText

minNumInstancesTipText

weightTrimBetaTipText

useAICTipText

getRevision

main