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Class weka.classifiers.BVDecompose

java.lang.Object
    |
    +----weka.classifiers.BVDecompose

public class BVDecompose
extends java.lang.Object
implements OptionHandler
Class for performing a Bias-Variance decomposition on any classifier using the method specified in:

R. Kohavi & D. Wolpert (1996), Bias plus variance decomposition for zero-one loss functions, in Proc. of the Thirteenth International Machine Learning Conference (ICML96) download postscript.

Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a learner to perform the decomposition on (required).

-t filename
Set the arff file to use for the decomposition (required).

-T num
Specify the number of instances in the training pool (default 100).

-c num
Specify the index of the class attribute (default last).

-x num
Set the number of train iterations (default 50).

-s num
Set the seed for the dataset randomisation (default 1).

Options after -- are passed to the designated sub-learner.

Version:
$Revision: 1.6 $
Author:
Len Trigg (trigg@cs.waikato.ac.nz)

Constructor Index

 o BVDecompose()
 

Method Index

 o decompose()
Carry out the bias-variance decomposition
 o getBias()
Get the calculated bias squared
 o getClassifier()
Gets the name of the classifier being analysed
 o getClassIndex()
Get the index (starting from 1) of the attribute used as the class.
 o getDataFileName()
Get the name of the data file used for the decomposition
 o getDebug()
Gets whether debugging is turned on
 o getError()
Get the calculated error rate
 o getOptions()
Gets the current settings of the CheckClassifier.
 o getSeed()
Gets the random number seed
 o getSigma()
Get the calculated sigma squared
 o getTrainIterations()
Gets the maximum number of boost iterations
 o getTrainPoolSize()
Get the number of instances in the training pool.
 o getVariance()
Get the calculated variance
 o listOptions()
Returns an enumeration describing the available options
 o main(String[])
Test method for this class
 o setClassifier(Classifier)
Set the classifiers being analysed
 o setClassIndex(int)
Sets index of attribute to discretize on
 o setDataFileName(String)
Sets the maximum number of boost iterations
 o setDebug(boolean)
Sets debugging mode
 o setOptions(String[])
Parses a given list of options.
 o setSeed(int)
Sets the random number seed
 o setTrainIterations(int)
Sets the maximum number of boost iterations
 o setTrainPoolSize(int)
Set the number of instances in the training pool.
 o toString()
Returns description of the bias-variance decomposition results.

Constructor Detail

 o BVDecompose
public BVDecompose()

Method Detail

 o listOptions
public java.util.Enumeration listOptions()
          Returns an enumeration describing the available options
Returns:
an enumeration of all the available options
 o setOptions
public void setOptions(java.lang.String options[]) throws java.lang.Exception
          Parses a given list of options. Valid options are:

-D
Turn on debugging output.

-W classname
Specify the full class name of a learner to perform the decomposition on (required).

-t filename
Set the arff file to use for the decomposition (required).

-T num
Specify the number of instances in the training pool (default 100).

-c num
Specify the index of the class attribute (default last).

-x num
Set the number of train iterations (default 50).

-s num
Set the seed for the dataset randomisation (default 1).

Options after -- are passed to the designated sub-learner.

Parameters:
options - the list of options as an array of strings
Throws:
java.lang.Exception - if an option is not supported
 o getOptions
public java.lang.String[] getOptions()
          Gets the current settings of the CheckClassifier.
Returns:
an array of strings suitable for passing to setOptions
 o getTrainPoolSize
public int getTrainPoolSize()
          Get the number of instances in the training pool.
Returns:
number of instances in the training pool.
 o setTrainPoolSize
public void setTrainPoolSize(int numTrain)
          Set the number of instances in the training pool.
Parameters:
numTrain - number of instances in the training pool.
 o setClassifier
public void setClassifier(Classifier newClassifier)
          Set the classifiers being analysed
Parameters:
newClassifier - the Classifier to use.
 o getClassifier
public Classifier getClassifier()
          Gets the name of the classifier being analysed
Returns:
the classifier being analysed.
 o setDebug
public void setDebug(boolean debug)
          Sets debugging mode
Parameters:
debug - true if debug output should be printed
 o getDebug
public boolean getDebug()
          Gets whether debugging is turned on
Returns:
true if debugging output is on
 o setSeed
public void setSeed(int seed)
          Sets the random number seed
 o getSeed
public int getSeed()
          Gets the random number seed
Returns:
the random number seed
 o setTrainIterations
public void setTrainIterations(int trainIterations)
          Sets the maximum number of boost iterations
 o getTrainIterations
public int getTrainIterations()
          Gets the maximum number of boost iterations
Returns:
the maximum number of boost iterations
 o setDataFileName
public void setDataFileName(java.lang.String dataFileName)
          Sets the maximum number of boost iterations
 o getDataFileName
public java.lang.String getDataFileName()
          Get the name of the data file used for the decomposition
Returns:
the name of the data file
 o getClassIndex
public int getClassIndex()
          Get the index (starting from 1) of the attribute used as the class.
Returns:
the index of the class attribute
 o setClassIndex
public void setClassIndex(int classIndex)
          Sets index of attribute to discretize on
Parameters:
index - the index (starting from 1) of the class attribute
 o getBias
public double getBias()
          Get the calculated bias squared
Returns:
the bias squared
 o getVariance
public double getVariance()
          Get the calculated variance
Returns:
the variance
 o getSigma
public double getSigma()
          Get the calculated sigma squared
Returns:
the sigma squared
 o getError
public double getError()
          Get the calculated error rate
Returns:
the error rate
 o decompose
public void decompose() throws java.lang.Exception
          Carry out the bias-variance decomposition
Throws:
java.lang.Exception - if the decomposition couldn't be carried out
 o toString
public java.lang.String toString()
          Returns description of the bias-variance decomposition results.
Returns:
the bias-variance decomposition results as a string
Overrides:
toString in class java.lang.Object
 o main
public static void main(java.lang.String args[])
          Test method for this class
Parameters:
args - the command line arguments

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