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Class weka.classifiers.LinearRegression 

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.LinearRegression
public class LinearRegression
extends Classifier
implements OptionHandler, WeightedInstancesHandler
Class for using linear regression for prediction. Uses the Akaike criterion for model selection, and is able to deal with weighted instances.

Valid options are:

-D
Produce debugging output.

-S num
Set the attriute selection method to use. 1 = None, 2 = Greedy (default 0 = M5' method)

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

Variable Index

o TAGS_SELECTION

Constructor Index

o LinearRegression()

Method Index

o buildClassifier(Instances)
Builds a regression model for the given data.
o classifyInstance(Instance)
Classifies the given instance using the linear regression function.
o getAttributeSelectionMethod()
Gets the method used to select attributes for use in the linear regression.
o getDebug()
Controls whether debugging output will be printed
o getOptions()
Gets the current settings of the classifier.
o listOptions()
Returns an enumeration describing the available options
o main(String[])
Generates a linear regression function predictor.
o numParameters()
Get the number of coefficients used in the model
o setAttributeSelectionMethod(SelectedTag)
Sets the method used to select attributes for use in the linear regression.
o setDebug(boolean)
Controls whether debugging output will be printed
o setOptions(String[])
Parses a given list of options.
o toString()
Outputs the linear regression model as a string.

Variables

o TAGS_SELECTION 
 public static final Tag TAGS_SELECTION[]

Constructors

o LinearRegression 
 public LinearRegression()

Methods

o buildClassifier 
 public void buildClassifier(Instances data) throws Exception
Builds a regression model for the given data.

Parameters:
data - the training data to be used for generating the linear regression function
Throws: Exception
if the classifier could not be built successfully
Overrides:
buildClassifier in class Classifier
o classifyInstance 
 public double classifyInstance(Instance instance) throws Exception
Classifies the given instance using the linear regression function.

Parameters:
instance - the test instance
Returns:
the classification
Throws: Exception
if classification can't be done successfully
Overrides:
classifyInstance in class Classifier
o toString 
 public String toString()
Outputs the linear regression model as a string.

Overrides:
toString in class Object
o listOptions 
 public Enumeration listOptions()
Returns an enumeration describing the available options

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

-D
Produce debugging output.

-S num
Set the attriute selection method to use. 1 = None, 2 = Greedy (default 0 = M5' method)

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
o getOptions 
 public String[] getOptions()
Gets the current settings of the classifier.

Returns:
an array of strings suitable for passing to setOptions
o numParameters 
 public int numParameters()
Get the number of coefficients used in the model

Returns:
the number of coefficients
o setAttributeSelectionMethod 
 public void setAttributeSelectionMethod(SelectedTag method)
Sets the method used to select attributes for use in the linear regression.

Parameters:
method - the attribute selection method to use.
o getAttributeSelectionMethod 
 public SelectedTag getAttributeSelectionMethod()
Gets the method used to select attributes for use in the linear regression.

Returns:
the method to use.
o setDebug 
 public void setDebug(boolean debug)
Controls whether debugging output will be printed

Parameters:
debug - true if debugging output should be printed
o getDebug 
 public boolean getDebug()
Controls whether debugging output will be printed

Parameters:
debug - true if debugging output should be printed
o main 
 public static void main(String argv[])
Generates a linear regression function predictor.

Parameters:
String - the options 
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