Stacking

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## weka.classifiers.meta
Class Stacking

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.MultipleClassifiersCombiner
              weka.classifiers.RandomizableMultipleClassifiersCombiner
                  weka.classifiers.meta.Stacking

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

Direct Known Subclasses:
  Grading, StackingC

    public class Stacking

extends RandomizableMultipleClassifiersCombiner
implements TechnicalInformationHandler

Combines several classifiers using the stacking method. Can do classification or regression.
For more information, see
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.

BibTeX:

     @article{Wolpert1992,
        author = {David H. Wolpert},
        journal = {Neural Networks},
        pages = {241-259},
        publisher = {Pergamon Press},
        title = {Stacked generalization},
        volume = {5},
        year = {1992}
     }

Valid options are:

     -M <scheme specification>
      Full name of meta classifier, followed by options.
      (default: "weka.classifiers.rules.Zero")

     -X <number of folds>
      Sets the number of cross-validation folds.

     -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: 1.32 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`Stacking()`

Method Summary
`void` | `buildClassifier(Instances data)` Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
`double[]` | `distributionForInstance(Instance instance)` Returns class probabilities.
`Capabilities` | `getCapabilities()` Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
`Classifier` | `getMetaClassifier()` Gets the meta classifier.
`int` | `getNumFolds()` Gets the number of folds for the cross-validation.
`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.
`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` | `metaClassifierTipText()` Returns the tip text for this property
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`void` | `setMetaClassifier(Classifier classifier)` Adds meta classifier
`void` | `setNumFolds(int numFolds)` Sets the number of folds for the cross-validation.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String` | `toString()` Output a representation of this classifier

Methods inherited from class weka.classifiers.RandomizableMultipleClassifiersCombiner
`getSeed, seedTipText, setSeed`

Methods inherited from class weka.classifiers.MultipleClassifiersCombiner
`classifiersTipText, getClassifier, getClassifiers, setClassifiers`

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

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

Constructor Detail

### Stacking

    public Stacking()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing classifier

  Returns:
    a description 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

### 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:

       -M <scheme specification>
        Full name of meta classifier, followed by options.
        (default: "weka.classifiers.rules.Zero")

       -X <number of folds>
        Sets the number of cross-validation folds.

       -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

### 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 the cross-validation.

  Returns:
    the number of folds for the cross-validation

### setNumFolds

    public void setNumFolds(int numFolds)
                     throws java.lang.Exception

  Sets the number of folds for the cross-validation.

  Parameters:
    `numFolds` - the number of folds for the cross-validation
  Throws:
    `java.lang.Exception` - if parameter illegal

### metaClassifierTipText

    public java.lang.String metaClassifierTipText()

  Returns the tip text for this property

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

### setMetaClassifier

    public void setMetaClassifier(Classifier classifier)

  Adds meta classifier

  Parameters:
    `classifier` - the classifier with all options set.

### getMetaClassifier

    public Classifier getMetaClassifier()

  Gets the meta classifier.

  Returns:
    the meta classifier

### getCapabilities

    public Capabilities getCapabilities()

  Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `MultipleClassifiersCombiner`
  Returns:
    the capabilities of the base classifiers
  See Also:
    `Capabilities`

### 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.

  Specified by:
    `buildClassifier` in class `Classifier`
  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.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    the distribution
  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`
  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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