Package weka.classifiers.trees

Class LMT

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.LMT
All Implemented Interfaces:
Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler
public class LMT extends AbstractClassifier 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.
 
 -doNotMakeSplitPointActualValue
  Do not make split point actual value.
Version:
$Revision: 15519 $
Author:
Niels Landwehr, Marc Sumner
See Also:

  • Constructor Details

    • LMT

      public LMT()
      Creates an instance of LMT with standard options

  • Method Details

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds the classifier.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      data - the data to train with
      Throws:
      Exception - if classifier can't be built successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Returns class probabilities for an instance.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to compute the distribution for
      Returns:
      the class probabilities
      Throws:
      Exception - if distribution can't be computed successfully

    • classifyInstance

      public double classifyInstance(Instance instance) throws Exception
      Classifies an instance.
      Specified by:
      classifyInstance in interface Classifier
      Overrides:
      classifyInstance in class AbstractClassifier
      Parameters:
      instance - the instance to classify
      Returns:
      the classification
      Throws:
      Exception - if instance can't be classified successfully

    • toString

      public String toString()
      Returns a description of the classifier.
      Overrides:
      toString in class Object
      Returns:
      a string representation of the classifier

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration of all the available options.

    • setOptions

      public void setOptions(String[] options) throws 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.
 
       -doNotMakeSplitPointActualValue
  Do not make split point actual value.
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of the Classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      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 String graph() throws Exception
      Returns graph describing the tree.
      Specified by:
      graph in interface Drawable
      Returns:
      the graph describing the tree
      Throws:
      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 Enumeration<String> 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(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:
      IllegalArgumentException - if the named measure is not supported

    • globalInfo

      public 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 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 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 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 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 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 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 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 String useAICTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • doNotMakeSplitPointActualValueTipText

      public String doNotMakeSplitPointActualValueTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • numDecimalPlacesTipText

      public String numDecimalPlacesTipText()
      Returns the tip text for this property
      Overrides:
      numDecimalPlacesTipText in class AbstractClassifier
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getDoNotMakeSplitPointActualValue

      public boolean getDoNotMakeSplitPointActualValue()
      Gets the value of doNotMakeSplitPointActualValue.
      Returns:
      the value

    • setDoNotMakeSplitPointActualValue

      public void setDoNotMakeSplitPointActualValue(boolean m_doNotMakeSplitPointActualValue)
      Sets the value of doNotMakeSplitPointActualValue.
      Parameters:
      m_doNotMakeSplitPointActualValue - the value to set

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class AbstractClassifier
      Returns:
      the revision

    • main

      public static void main(String[] argv)
      Main method for testing this class
      Parameters:
      argv - the commandline options