Package weka.classifiers.trees.lmt

Class LogisticBase

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.lmt.LogisticBase
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler
Direct Known Subclasses:
LMTNode
public class LogisticBase extends AbstractClassifier implements WeightedInstancesHandler
Base/helper class for building logistic regression models with the LogitBoost algorithm. Used for building logistic model trees (weka.classifiers.trees.lmt.LMT) and standalone logistic regression (weka.classifiers.functions.SimpleLogistic). Valid options are: 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
Version:
$Revision: 14202 $
Author:
Niels Landwehr, Marc Sumner
See Also:

  • Constructor Details

    • LogisticBase

      public LogisticBase()
      Constructor that creates LogisticBase object with standard options.

    • LogisticBase

      public LogisticBase(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities)
      Constructor to create LogisticBase object.
      Parameters:
      numBoostingIterations - fixed number of iterations for LogitBoost (if negative, use cross-validation or stopping criterion on the training data).
      useCrossValidation - cross-validate number of LogitBoost iterations (if false, use stopping criterion on the training data).
      errorOnProbabilities - if true, use error on probabilities instead of misclassification for stopping criterion of LogitBoost

  • Method Details

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds the logistic regression model usiing LogitBoost.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      data - the training data
      Throws:
      Exception - if something goes wrong

    • getUsedAttributes

      public int[][] getUsedAttributes()
      Returns an array of the indices of the attributes used in the logistic model. The first dimension is the class, the second dimension holds a list of attribute indices. Attribute indices start at zero.
      Returns:
      the array of attribute indices

    • getNumRegressions

      public int getNumRegressions()
      The number of LogitBoost iterations performed (= the number of simple regression functions fit).
      Returns:
      the number of LogitBoost iterations performed

    • getWeightTrimBeta

      public double getWeightTrimBeta()
      Get the value of weightTrimBeta.
      Returns:
      Value of weightTrimBeta.

    • getUseAIC

      public boolean getUseAIC()
      Get the value of useAIC.
      Returns:
      Value of useAIC.

    • setMaxIterations

      public void setMaxIterations(int maxIterations)
      Sets the parameter "maxIterations".
      Parameters:
      maxIterations - the maximum iterations

    • setHeuristicStop

      public void setHeuristicStop(int heuristicStop)
      Sets the option "heuristicStop".
      Parameters:
      heuristicStop - the heuristic stop to use

    • setWeightTrimBeta

      public void setWeightTrimBeta(double w)
      Sets the option "weightTrimBeta".

    • setUseAIC

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

    • getMaxIterations

      public int getMaxIterations()
      Returns the maxIterations parameter.
      Returns:
      the maximum iteration

    • percentAttributesUsed

      public double percentAttributesUsed()
      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.
      Returns:
      the fraction of all attributes that are used

    • toString

      public String toString()
      Returns a description of the logistic model (i.e., attributes and coefficients).
      Overrides:
      toString in class Object
      Returns:
      the description of the model

    • 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

    • cleanup

      public void cleanup()
      Cleanup in order to save memory.

    • getRevision

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