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## weka.classifiers.trees
Class LADTree

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.trees.LADTree

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

    public class LADTree

extends AbstractClassifier
implements Drawable, AdditionalMeasureProducer, TechnicalInformationHandler

Class for generating a multi-class alternating decision tree using the LogitBoost strategy. For more info, see
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

BibTeX:

     @inproceedings{Holmes2001,
        author = {Geoffrey Holmes and Bernhard Pfahringer and Richard Kirkby and Eibe Frank and Mark Hall},
        booktitle = {ECML},
        pages = {161-172},
        publisher = {Springer},
        title = {Multiclass alternating decision trees},
        year = {2001}
     }

Valid options are:

     -B <number of boosting iterations>
      Number of boosting iterations.
      (Default = 10)

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

Version:
  $Revision: 6035 $
Author:
  Richard Kirkby
See Also:
  Serialized Form

Field Summary

Fields inherited from interface weka.core.Drawable
`BayesNet, Newick, NOT_DRAWABLE, TREE`

Constructor Summary
`LADTree()`

Method Summary
`void` | `buildClassifier(Instances instances)` Builds a classifier for a set of instances.
`double[]` | `distributionForInstance(Instance instance)` Returns the class probability distribution for an instance.
`void` | `done()`
`java.util.Enumeration` | `enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`int` | `getNumOfBoostingIterations()` Gets the number of boosting iterations.
`java.lang.String[]` | `getOptions()` Gets the current settings of ADTree.
`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.lang.String` | `graph()` Returns graph describing the tree.
`int` | `graphType()` Returns the type of graph this classifier represents.
`void` | `initClassifier(Instances instances)` Sets up the tree ready to be trained.
`java.lang.String` | `legend()` Returns the legend of the tree, describing how results are to be interpreted.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`double` | `measureExamplesCounted()` Returns the number of examples "counted".
`double` | `measureNodesExpanded()` Returns the number of nodes expanded.
`double` | `measureNumLeaves()` Calls measure function for leaf size.
`double` | `measureNumPredictionLeaves()` Calls measure function for leaf size.
`double` | `measureTreeSize()` Calls measure function for tree size.
`void` | `merge(LADTree mergeWith)` Merges two trees together.
`void` | `next(int iteration)`
`java.lang.String` | `numOfBoostingIterationsTipText()`
`int` | `predictiveError(Instances test)`
`void` | `setNumOfBoostingIterations(int b)` Sets the number of boosting iterations.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String` | `toString()` Returns a description of the classifier.

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

### LADTree

    public LADTree()

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

### initClassifier

    public void initClassifier(Instances instances)
                        throws java.lang.Exception

  Sets up the tree ready to be trained.

  Parameters:
    `instances` - the instances to train the tree with
  Throws:
    `java.lang.Exception` - if training data is unsuitable

### next

    public void next(int iteration)
              throws java.lang.Exception

  Throws:
    `java.lang.Exception`

### done

    public void done()
              throws java.lang.Exception

  Throws:
    `java.lang.Exception`

### distributionForInstance

    public double[] distributionForInstance(Instance instance)

  Returns the class probability distribution for an instance.

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    the distribution the tree generates for the instance

### toString

    public java.lang.String toString()

  Returns a description of the classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a string containing a description of the classifier

### graph

    public java.lang.String graph()
                           throws java.lang.Exception

  Returns graph describing the tree.

  Specified by:
    `graph` in interface `Drawable`
  Returns:
    the graph of the tree in dotty format
  Throws:
    `java.lang.Exception` - if something goes wrong

### legend

    public java.lang.String legend()

  Returns the legend of the tree, describing how results are to be interpreted.

  Returns:
    a string containing the legend of the classifier

### numOfBoostingIterationsTipText

    public java.lang.String numOfBoostingIterationsTipText()

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

### getNumOfBoostingIterations

    public int getNumOfBoostingIterations()

  Gets the number of boosting iterations.

  Returns:
    the number of boosting iterations

### setNumOfBoostingIterations

    public void setNumOfBoostingIterations(int b)

  Sets the number of boosting iterations.

  Parameters:
    `b` - the number of boosting iterations to use

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

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

  -B num
  Set the number of boosting iterations (default 10)

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `AbstractClassifier`
  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 ADTree.

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

### measureTreeSize

    public double measureTreeSize()

  Calls measure function for tree size.

  Returns:
    the tree size

### measureNumLeaves

    public double measureNumLeaves()

  Calls measure function for leaf size.

  Returns:
    the leaf size

### measureNumPredictionLeaves

    public double measureNumPredictionLeaves()

  Calls measure function for leaf size.

  Returns:
    the leaf size

### measureNodesExpanded

    public double measureNodesExpanded()

  Returns the number of nodes expanded.

  Returns:
    the number of nodes expanded during search

### measureExamplesCounted

    public double measureExamplesCounted()

  Returns the number of examples "counted".

  Returns:
    the number of nodes processed during search

### enumerateMeasures

    public java.util.Enumeration enumerateMeasures()

  Returns an enumeration of the additional measure names.

  Specified by:
    `enumerateMeasures` in interface `AdditionalMeasureProducer`
  Returns:
    an enumeration of the measure names

### getMeasure

    public double getMeasure(java.lang.String additionalMeasureName)

  Returns the value of the named measure.

  Specified by:
    `getMeasure` in interface `AdditionalMeasureProducer`
  Parameters:
    `additionalMeasureName` - the name of the measure to query for its value
  Returns:
    the value of the named measure
  Throws:
    `java.lang.IllegalArgumentException` - if the named measure is not supported

### buildClassifier

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

  Builds a classifier for a set of instances.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `instances` - the instances to train the classifier with
  Throws:
    `java.lang.Exception` - if something goes wrong

### predictiveError

    public int predictiveError(Instances test)

### merge

    public void merge(LADTree mergeWith)
               throws java.lang.Exception

  Merges two trees together. Modifies the tree being acted on, leaving tree passed as a parameter untouched (cloned). Does not check to see whether training instances are compatible - strange things could occur if they are not.

  Parameters:
    `mergeWith` - the tree to merge with
  Throws:
    `java.lang.Exception` - if merge could not be performed

### graphType

    public int graphType()

  Returns the type of graph this classifier represents.

  Specified by:
    `graphType` in interface `Drawable`
  Returns:
    Drawable.TREE

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

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

### 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 `AbstractClassifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### main

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

  Main method for testing this class.

  Parameters:
    `argv` - the options

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