weka.classifiers.trees.lmt
Class LogisticBase

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.trees.lmt.LogisticBase

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable, WeightedInstancesHandler

Direct Known Subclasses:

LMTNode

public class LogisticBase

extends Classifier

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).

Version:

$Revision: 1.2 $

Author:

Niels Landwehr

See Also:

Serialized Form

Constructor Summary

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

LogisticBase(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities)
Constructor to create LogisticBase object.

Method Summary

void	buildClassifier(Instances data) Builds the logistic regression model usiing LogitBoost.
void	cleanup() Cleanup in order to save memory.
double[]	distributionForInstance(Instance instance) Returns class probabilities for an instance.
int	getMaxIterations() Returns the maxIterations parameter.
int	getNumRegressions() The number of LogitBoost iterations performed (= the number of simple regression functions fit).
int[][]	getUsedAttributes() Returns an array of the indices of the attributes used in the logistic model.
double	percentAttributesUsed() Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
void	setHeuristicStop(int heuristicStop) Sets the option "heuristicStop".
void	setMaxIterations(int maxIterations) Sets the parameter "maxIterations".
java.lang.String	toString() Returns a description of the logistic model (i.e., attributes and coefficients).

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

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 Detail

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Builds the logistic regression model usiing LogitBoost.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if the classifier has not been generated successfully

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).

setMaxIterations

public void setMaxIterations(int maxIterations)

Sets the parameter "maxIterations".

setHeuristicStop

public void setHeuristicStop(int heuristicStop)

Sets the option "heuristicStop".

getMaxIterations

public int getMaxIterations()

Returns the maxIterations parameter.

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.

toString

public java.lang.String toString()

Returns a description of the logistic model (i.e., attributes and coefficients).

Overrides:

toString in class java.lang.Object

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 be classified

Returns:

an array containing the estimated membership probabilities of the test instance in each class or the numeric prediction

Throws:

java.lang.Exception - if distribution can't be computed successfully

cleanup

public void cleanup()

Cleanup in order to save memory.