类 SimpleLogistic

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.functions.SimpleLogistic
所有已实现的接口:
Serializable, Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic models. The optimal number of LogitBoost iterations to perform is cross-validated, which leads to automatic attribute selection. For more information see:
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

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},
    booktitle = {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:

 -I <iterations>
  Set fixed number of iterations for LogitBoost
 -S
  Use stopping criterion on training set (instead of
  cross-validation)
 -P
  Use error on probabilities (rmse) instead of
  misclassification error for stopping criterion
 -M <iterations>
  Set maximum number of boosting iterations
 -H <iterations>
  Set parameter for heuristic for early stopping of
  LogitBoost.
  If enabled, the minimum is selected greedily, stopping
  if the current minimum has not changed for iter iterations.
  By default, heuristic is enabled with value 50. Set to
  zero to disable heuristic.
 -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
 
 -A
  The AIC is used to choose the best iteration (instead of CV or training error).
 
版本:
$Revision: 5523 $
作者:
Niels Landwehr, Marc Sumner
另请参阅:
  • 构造器详细资料

    • SimpleLogistic

      public SimpleLogistic()
      Constructor for creating SimpleLogistic object with standard options.
    • SimpleLogistic

      public SimpleLogistic(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities)
      Constructor for creating SimpleLogistic object.
      参数:
      numBoostingIterations - if non-negative, use this as fixed number of iterations for LogitBoost
      useCrossValidation - cross-validate number of LogitBoost iterations.
      errorOnProbabilities - minimize error on probabilities instead of misclassification error
  • 方法详细资料

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      指定者:
      getCapabilities 在接口中 CapabilitiesHandler
      覆盖:
      getCapabilities 在类中 Classifier
      返回:
      the capabilities of this classifier
      另请参阅:
    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds the logistic regression using LogitBoost.
      指定者:
      buildClassifier 在类中 Classifier
      参数:
      data - the training data
      抛出:
      Exception - if something goes wrong
    • distributionForInstance

      public double[] distributionForInstance(Instance inst) throws Exception
      Returns class probabilities for an instance.
      覆盖:
      distributionForInstance 在类中 Classifier
      参数:
      inst - the instance to compute the probabilities for
      返回:
      the probabilities
      抛出:
      Exception - if distribution can't be computed successfully
    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options.
      指定者:
      listOptions 在接口中 OptionHandler
      覆盖:
      listOptions 在类中 Classifier
      返回:
      an enumeration of all the available options.
    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are:

       -I <iterations>
        Set fixed number of iterations for LogitBoost
       -S
        Use stopping criterion on training set (instead of
        cross-validation)
       -P
        Use error on probabilities (rmse) instead of
        misclassification error for stopping criterion
       -M <iterations>
        Set maximum number of boosting iterations
       -H <iterations>
        Set parameter for heuristic for early stopping of
        LogitBoost.
        If enabled, the minimum is selected greedily, stopping
        if the current minimum has not changed for iter iterations.
        By default, heuristic is enabled with value 50. Set to
        zero to disable heuristic.
       -W <beta>
        Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
       
       -A
        The AIC is used to choose the best iteration (instead of CV or training error).
       
      指定者:
      setOptions 在接口中 OptionHandler
      覆盖:
      setOptions 在类中 Classifier
      参数:
      options - the list of options as an array of strings
      抛出:
      Exception - if an option is not supported
    • getOptions

      public String[] getOptions()
      Gets the current settings of the Classifier.
      指定者:
      getOptions 在接口中 OptionHandler
      覆盖:
      getOptions 在类中 Classifier
      返回:
      an array of strings suitable for passing to setOptions
    • getNumBoostingIterations

      public int getNumBoostingIterations()
      Get the value of numBoostingIterations.
      返回:
      the number of boosting iterations
    • getUseCrossValidation

      public boolean getUseCrossValidation()
      Get the value of useCrossValidation.
      返回:
      true if cross-validation is used
    • getErrorOnProbabilities

      public boolean getErrorOnProbabilities()
      Get the value of errorOnProbabilities.
      返回:
      If true, use minimize error on probabilities instead of misclassification error
    • getMaxBoostingIterations

      public int getMaxBoostingIterations()
      Get the value of maxBoostingIterations.
      返回:
      the maximum number of boosting iterations
    • getHeuristicStop

      public int getHeuristicStop()
      Get the value of heuristicStop.
      返回:
      the value of heuristicStop
    • getWeightTrimBeta

      public double getWeightTrimBeta()
      Get the value of weightTrimBeta.
    • getUseAIC

      public boolean getUseAIC()
      Get the value of useAIC.
      返回:
      Value of useAIC.
    • setNumBoostingIterations

      public void setNumBoostingIterations(int n)
      Set the value of numBoostingIterations.
      参数:
      n - the number of boosting iterations
    • setUseCrossValidation

      public void setUseCrossValidation(boolean l)
      Set the value of useCrossValidation.
      参数:
      l - whether to use cross-validation
    • setErrorOnProbabilities

      public void setErrorOnProbabilities(boolean l)
      Set the value of errorOnProbabilities.
      参数:
      l - If true, use minimize error on probabilities instead of misclassification error
    • setMaxBoostingIterations

      public void setMaxBoostingIterations(int n)
      Set the value of maxBoostingIterations.
      参数:
      n - the maximum number of boosting iterations
    • setHeuristicStop

      public void setHeuristicStop(int n)
      Set the value of heuristicStop.
      参数:
      n - the value of heuristicStop
    • setWeightTrimBeta

      public void setWeightTrimBeta(double n)
      Set the value of weightTrimBeta.
    • setUseAIC

      public void setUseAIC(boolean c)
      Set the value of useAIC.
      参数:
      c - Value to assign to useAIC.
    • getNumRegressions

      public int getNumRegressions()
      Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
      返回:
      the number of LogitBoost iterations performed
    • toString

      public String toString()
      Returns a description of the logistic model (attributes/coefficients).
      覆盖:
      toString 在类中 Object
      返回:
      the model as string
    • measureAttributesUsed

      public double measureAttributesUsed()
      Returns the fraction of all attributes in the data that are used in the logistic model (in percent). An attribute is used in the model if it is used in any of the models for the different classes.
      返回:
      percentage of attributes used in the model
    • enumerateMeasures

      public Enumeration enumerateMeasures()
      Returns an enumeration of the additional measure names
      指定者:
      enumerateMeasures 在接口中 AdditionalMeasureProducer
      返回:
      an enumeration of the measure names
    • getMeasure

      public double getMeasure(String additionalMeasureName)
      Returns the value of the named measure
      指定者:
      getMeasure 在接口中 AdditionalMeasureProducer
      参数:
      additionalMeasureName - the name of the measure to query for its value
      返回:
      the value of the named measure
      抛出:
      IllegalArgumentException - if the named measure is not supported
    • globalInfo

      public String globalInfo()
      Returns a string describing classifier
      返回:
      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.
      指定者:
      getTechnicalInformation 在接口中 TechnicalInformationHandler
      返回:
      the technical information about this class
    • numBoostingIterationsTipText

      public String numBoostingIterationsTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • useCrossValidationTipText

      public String useCrossValidationTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • errorOnProbabilitiesTipText

      public String errorOnProbabilitiesTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • maxBoostingIterationsTipText

      public String maxBoostingIterationsTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • heuristicStopTipText

      public String heuristicStopTipText()
      Returns the tip text for this property
      返回:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • weightTrimBetaTipText

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

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

      public String getRevision()
      Returns the revision string.
      指定者:
      getRevision 在接口中 RevisionHandler
      覆盖:
      getRevision 在类中 Classifier
      返回:
      the revision
    • main

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