weka.classifiers.trees

Class Id3

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.trees.Id3

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Sourcable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class Id3
extends Classifier
implements TechnicalInformationHandler, Sourcable

Class for constructing an unpruned decision tree based on the ID3 algorithm. Can only deal with nominal attributes. No missing values allowed. Empty leaves may result in unclassified instances. For more information see:

R. Quinlan (1986). Induction of decision trees. Machine Learning. 1(1):81-106.

BibTeX:

 @article{Quinlan1986,
    author = {R. Quinlan},
    journal = {Machine Learning},
    number = {1},
    pages = {81-106},
    title = {Induction of decision trees},
    volume = {1},
    year = {1986}
 }
 

Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:

$Revision: 6404 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Id3()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Builds Id3 decision tree classifier.
double	classifyInstance(Instance instance) Classifies a given test instance using the decision tree.
double[]	distributionForInstance(Instance instance) Computes class distribution for instance using decision tree.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String	getRevision() Returns the revision string.
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.
java.lang.String	globalInfo() Returns a string describing the classifier.
static void	main(java.lang.String[] args) Main method.
java.lang.String	toSource(java.lang.String className) Returns a string that describes the classifier as source.
java.lang.String	toString() Prints the decision tree using the private toString method from below.

Methods inherited from class weka.classifiers.Classifier

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

Methods inherited from class java.lang.Object

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

Constructor Detail

Id3

public Id3()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing the classifier.

Returns:

a description suitable for the 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.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class Classifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

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

Builds Id3 decision tree classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if classifier can't be built successfully

classifyInstance

public double classifyInstance(Instance instance)
                        throws NoSupportForMissingValuesException

Classifies a given test instance using the decision tree.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the classification

Throws:

NoSupportForMissingValuesException - if instance has missing values

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws NoSupportForMissingValuesException

Computes class distribution for instance using decision tree.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance for which distribution is to be computed

Returns:

the class distribution for the given instance

Throws:

NoSupportForMissingValuesException - if instance has missing values

toString

public java.lang.String toString()

Prints the decision tree using the private toString method from below.

Overrides:

toString in class java.lang.Object

Returns:

a textual description of the classifier

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns a string that describes the classifier as source. The classifier will be contained in a class with the given name (there may be auxiliary classes), and will contain a method with the signature:

 public static double classify(Object[] i);
 

where the array i contains elements that are either Double, String, with missing values represented as null. The generated code is public domain and comes with no warranty.
Note: works only if class attribute is the last attribute in the dataset.

Specified by:

toSource in interface Sourcable

Parameters:

className - the name that should be given to the source class.

Returns:

the object source described by a string

Throws:

java.lang.Exception - if the source can't be computed

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Classifier

Returns:

the revision

main

public static void main(java.lang.String[] args)

Main method.

Parameters:

args - the options for the classifier