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## weka.classifiers.meta
Class MultiScheme

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.MultipleClassifiersCombiner
              weka.classifiers.RandomizableMultipleClassifiersCombiner
                  weka.classifiers.meta.MultiScheme

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

    public class MultiScheme

extends RandomizableMultipleClassifiersCombiner

Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is measured based on percent correct (classification) or mean-squared error (regression).

Valid options are:

     -X <number of folds>
      Use cross validation for model selection using the
      given number of folds. (default 0, is to
      use training error)

     -S <num>
      Random number seed.
      (default 1)

     -B <classifier specification>
      Full class name of classifier to include, followed
      by scheme options. May be specified multiple times.
      (default: "weka.classifiers.rules.ZeroR")

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

Version:
  $Revision: 5928 $
Author:
  Len Trigg (trigg@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`MultiScheme()`

Method Summary
`void` | `buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`java.lang.String` | `classifiersTipText()` Returns the tip text for this property
`java.lang.String` | `debugTipText()` Returns the tip text for this property
`double[]` | `distributionForInstance(Instance instance)` Returns class probabilities.
`int` | `getBestClassifierIndex()` Get the index of the classifier that was determined as best during cross-validation.
`Classifier` | `getClassifier(int index)` Gets a single classifier from the set of available classifiers.
`Classifier[]` | `getClassifiers()` Gets the list of possible classifers to choose from.
`boolean` | `getDebug()` Get whether debugging is turned on
`int` | `getNumFolds()` Gets the number of folds for cross-validation.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getSeed()` Gets the random number seed.
`java.lang.String` | `globalInfo()` Returns a string describing classifier
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setClassifiers(Classifier[] classifiers)` Sets the list of possible classifers to choose from.
`void` | `setDebug(boolean debug)` Set debugging mode
`void` | `setNumFolds(int numFolds)` Sets the number of folds for cross-validation.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setSeed(int seed)` Sets the seed for random number generation.
`java.lang.String` | `toString()` Output a representation of this classifier

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner
`getCapabilities`

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

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

Constructor Detail

### MultiScheme

    public MultiScheme()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

  Returns:
    a description suitable for displaying in the explorer/experimenter gui

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `RandomizableMultipleClassifiersCombiner`
  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:

       -X <number of folds>
        Use cross validation for model selection using the
        given number of folds. (default 0, is to
        use training error)

       -S <num>
        Random number seed.
        (default 1)

       -B <classifier specification>
        Full class name of classifier to include, followed
        by scheme options. May be specified multiple times.
        (default: "weka.classifiers.rules.ZeroR")

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

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

### classifiersTipText

    public java.lang.String classifiersTipText()

  Returns the tip text for this property

  Overrides:
    `classifiersTipText` in class `MultipleClassifiersCombiner`
  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setClassifiers

    public void setClassifiers(Classifier[] classifiers)

  Sets the list of possible classifers to choose from.

  Overrides:
    `setClassifiers` in class `MultipleClassifiersCombiner`
  Parameters:
    `classifiers` - an array of classifiers with all options set.

### getClassifiers

    public Classifier[] getClassifiers()

  Gets the list of possible classifers to choose from.

  Overrides:
    `getClassifiers` in class `MultipleClassifiersCombiner`
  Returns:
    the array of Classifiers

### getClassifier

    public Classifier getClassifier(int index)

  Gets a single classifier from the set of available classifiers.

  Overrides:
    `getClassifier` in class `MultipleClassifiersCombiner`
  Parameters:
    `index` - the index of the classifier wanted
  Returns:
    the Classifier

### seedTipText

    public java.lang.String seedTipText()

  Returns the tip text for this property

  Overrides:
    `seedTipText` in class `RandomizableMultipleClassifiersCombiner`
  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setSeed

    public void setSeed(int seed)

  Sets the seed for random number generation.

  Specified by:
    `setSeed` in interface `Randomizable`
  Overrides:
    `setSeed` in class `RandomizableMultipleClassifiersCombiner`
  Parameters:
    `seed` - the random number seed

### getSeed

    public int getSeed()

  Gets the random number seed.

  Specified by:
    `getSeed` in interface `Randomizable`
  Overrides:
    `getSeed` in class `RandomizableMultipleClassifiersCombiner`
  Returns:
    the random number seed

### numFoldsTipText

    public java.lang.String numFoldsTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getNumFolds

    public int getNumFolds()

  Gets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation.

  Returns:
    the number of folds for cross-validation

### setNumFolds

    public void setNumFolds(int numFolds)

  Sets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation.

  Parameters:
    `numFolds` - the number of folds for cross-validation

### debugTipText

    public java.lang.String debugTipText()

  Returns the tip text for this property

  Overrides:
    `debugTipText` in class `AbstractClassifier`
  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setDebug

    public void setDebug(boolean debug)

  Set debugging mode

  Overrides:
    `setDebug` in class `AbstractClassifier`
  Parameters:
    `debug` - true if debug output should be printed

### getDebug

    public boolean getDebug()

  Get whether debugging is turned on

  Overrides:
    `getDebug` in class `AbstractClassifier`
  Returns:
    true if debugging output is on

### getBestClassifierIndex

    public int getBestClassifierIndex()

  Get the index of the classifier that was determined as best during cross-validation.

  Returns:
    the index in the classifier array

### buildClassifier

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

  Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.

  Parameters:
    `data` - the training data to be used for generating the boosted classifier.
  Throws:
    `java.lang.Exception` - if the classifier could not be built successfully

### distributionForInstance

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

  Returns class probabilities.

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    the distribution for the instance
  Throws:
    `java.lang.Exception` - if instance could not be classified successfully

### toString

    public java.lang.String toString()

  Output a representation of this classifier

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a string representation of the 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` - should contain the following arguments: -t training file [-T test file] [-c class index]

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