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## weka.classifiers.meta
Class OrdinalClassClassifier

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.meta.OrdinalClassClassifier

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class OrdinalClassClassifier

extends SingleClassifierEnhancer
implements OptionHandler, TechnicalInformationHandler

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems.
For more information see:
Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001.
Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

BibTeX:

     @inproceedings{Frank2001,
        author = {Eibe Frank and Mark Hall},
        booktitle = {12th European Conference on Machine Learning},
        pages = {145-156},
        publisher = {Springer},
        title = {A Simple Approach to Ordinal Classification},
        year = {2001}
     }

     @inproceedings{Schapire2002,
        author = {Robert E. Schapire and Peter Stone and David A. McAllester and Michael L. Littman and Janos A. Csirik},
        booktitle = {Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002)},
        pages = {546-553},
        publisher = {Morgan Kaufmann},
        title = {Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation},
        year = {2002}
     }

Valid options are:

     -S
      Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).

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

     -W
      Full name of base classifier.
      (default: weka.classifiers.trees.J48)

     Options specific to classifier weka.classifiers.trees.J48:

     -U
      Use unpruned tree.

     -C <pruning confidence>
      Set confidence threshold for pruning.
      (default 0.25)

     -M <minimum number of instances>
      Set minimum number of instances per leaf.
      (default 2)

     -R
      Use reduced error pruning.

     -N <number of folds>
      Set number of folds for reduced error
      pruning. One fold is used as pruning set.
      (default 3)

     -B
      Use binary splits only.

     -S
      Don't perform subtree raising.

     -L
      Do not clean up after the tree has been built.

     -A
      Laplace smoothing for predicted probabilities.

     -Q <seed>
      Seed for random data shuffling (default 1).

Version:
  $Revision: 5928 $
Author:
  Mark Hall, Eibe Frank
See Also:
  `OptionHandler`, Serialized Form

Constructor Summary
`OrdinalClassClassifier()` Default constructor.

Method Summary
`void` | `buildClassifier(Instances insts)` Builds the classifiers.
`double[]` | `distributionForInstance(Instance inst)` Returns the distribution for an instance.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`java.lang.String[]` | `getOptions()` Gets the current settings 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.
`boolean` | `getUseSmoothing()` Checks whether Schapire et al.'s smoothing method is used.
`java.lang.String` | `globalInfo()` Returns a string describing this attribute evaluator
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setUseSmoothing(boolean b)` Determines whether Schapire et al.'s smoothing method is used.
`java.lang.String` | `toString()` Prints the classifiers.
`java.lang.String` | `useSmoothingTipText()` Tip text method.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
`classifierTipText, getClassifier, setClassifier`

Methods inherited from class weka.classifiers.AbstractClassifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

### OrdinalClassClassifier

    public OrdinalClassClassifier()

  Default constructor.

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this attribute evaluator

  Returns:
    a description of the evaluator suitable for displaying in the explorer/experimenter 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 `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `SingleClassifierEnhancer`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

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

  Builds the classifiers.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `insts` - the training data.
  Throws:
    `java.lang.Exception` - if a classifier can't be built

### distributionForInstance

    public double[] distributionForInstance(Instance inst)
                                     throws java.lang.Exception

  Returns the distribution for an instance.

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `inst` - the instance to compute the distribution for
  Returns:
    the class distribution for the given instance
  Throws:
    `java.lang.Exception` - if the distribution can't be computed successfully

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `SingleClassifierEnhancer`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options.

  Valid options are:

       -S
        Turn off Schapire et al.'s smoothing heuristic (ICML02, pp. 550).

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

       -W
        Full name of base classifier.
        (default: weka.classifiers.trees.J48)

       Options specific to classifier weka.classifiers.trees.J48:

       -U
        Use unpruned tree.

       -C <pruning confidence>
        Set confidence threshold for pruning.
        (default 0.25)

       -M <minimum number of instances>
        Set minimum number of instances per leaf.
        (default 2)

       -R
        Use reduced error pruning.

       -N <number of folds>
        Set number of folds for reduced error
        pruning. One fold is used as pruning set.
        (default 3)

       -B
        Use binary splits only.

       -S
        Don't perform subtree raising.

       -L
        Do not clean up after the tree has been built.

       -A
        Laplace smoothing for predicted probabilities.

       -Q <seed>
        Seed for random data shuffling (default 1).

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `SingleClassifierEnhancer`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `SingleClassifierEnhancer`
  Returns:
    an array of strings suitable for passing to setOptions

### useSmoothingTipText

    public java.lang.String useSmoothingTipText()

  Tip text method.

  Returns:
    a tip text string suitable for displaying as a popup in the GUI.

### setUseSmoothing

    public void setUseSmoothing(boolean b)

  Determines whether Schapire et al.'s smoothing method is used.

  Parameters:
    `b` - true if the smoothing heuristic is to be used.

### getUseSmoothing

    public boolean getUseSmoothing()

  Checks whether Schapire et al.'s smoothing method is used.

  Returns:
    true if the smoothing heuristic is to be used.

### toString

    public java.lang.String toString()

  Prints the classifiers.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a string representation of this classifier

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `AbstractClassifier`
  Returns:
    the revision

### main

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

  Main method for testing this class.

  Parameters:
    `argv` - the options

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