ADTree (weka-stable 3.6.8 API)

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.trees.ADTree

All Implemented Interfaces:

Serializable, Cloneable, IterativeClassifier, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class ADTree
extends Classifier
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler, IterativeClassifier, TechnicalInformationHandler
```
Class for generating an alternating decision tree. The basic algorithm is based on:

Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.

This version currently only supports two-class problems. The number of boosting iterations needs to be manually tuned to suit the dataset and the desired complexity/accuracy tradeoff. Induction of the trees has been optimized, and heuristic search methods have been introduced to speed learning.
BibTeX:
```
 @inproceedings{Freund1999,
    address = {Bled, Slovenia},
    author = {Freund, Y. and Mason, L.},
    booktitle = {Proceeding of the Sixteenth International Conference on Machine Learning},
    pages = {124-133},
    title = {The alternating decision tree learning algorithm},
    year = {1999}
 }
 
```
Valid options are:
```
 -B <number of boosting iterations>
  Number of boosting iterations.
  (Default = 10)
```
```
 -E <-3|-2|-1|>=0>
  Expand nodes: -3(all), -2(weight), -1(z_pure), >=0 seed for random walk
  (Default = -3)
```
```
 -D
  Save the instance data with the model
```
Version:

$Revision: 5535 $

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz), Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected int`	`m_boostingIterations` Option - the number of boosting iterations o perform
`protected int`	`m_examplesCounted` Statistics - the number of instances processed during search
`protected int`	`m_lastAddedSplitNum` The number of the last splitter added to the tree
`protected ReferenceInstances`	`m_negTrainInstances` The training instances with negative class - referencing the training dataset
`protected int`	`m_nodesExpanded` Statistics - the number of prediction nodes investigated during search
`protected int[]`	`m_nominalAttIndices` An array containing the inidices to the nominal attributes in the data
`protected int[]`	`m_numericAttIndices` An array containing the inidices to the numeric attributes in the data
`protected ReferenceInstances`	`m_posTrainInstances` The training instances with positive class - referencing the training dataset
`protected Random`	`m_random` The random number generator - used for the random search heuristic
`protected int`	`m_randomSeed` Option - the seed to use for a random search
`protected PredictionNode`	`m_root` The root of the tree
`protected boolean`	`m_saveInstanceData` Option - whether the tree should remember the instance data
`protected PredictionNode`	`m_search_bestInsertionNode` The best node to insert under, as found so far by the latest search
`protected Instances`	`m_search_bestPathNegInstances` The negative instances that apply to the best path found so far
`protected Instances`	`m_search_bestPathPosInstances` The positive instances that apply to the best path found so far
`protected Splitter`	`m_search_bestSplitter` The best splitter to insert, as found so far by the latest search
`protected double`	`m_search_smallestZ` The smallest Z value found so far by the latest search
`protected int`	`m_searchPath` Option - the search mode
`protected Instances`	`m_trainInstances` The instances used to train the tree
`protected double`	`m_trainTotalWeight` The total weight of the instances - used to speed Z calculations
`static int`	`SEARCHPATH_ALL` search mode: Expand all paths
`static int`	`SEARCHPATH_HEAVIEST` search mode: Expand the heaviest path
`static int`	`SEARCHPATH_RANDOM` search mode: Expand a random path
`static int`	`SEARCHPATH_ZPURE` search mode: Expand the best z-pure path
`static Tag[]`	`TAGS_SEARCHPATH` The search modes

Fields inherited from class weka.classifiers.Classifier
m_Debug

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

Constructor Summary

Constructors
Constructor and Description

ADTree()

Constructors
Constructor and Description
`ADTree()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`boost()` Performs a single boosting iteration, using two-class optimized method.
`void`	`buildClassifier(Instances instances)` Builds a classifier for a set of instances.
`Object`	`clone()` Creates a clone that is identical to the current tree, but is independent.
`double[]`	`distributionForInstance(Instance instance)` Returns the class probability distribution for an instance.
`void`	`done()` Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`int`	`getNumOfBoostingIterations()` Gets the number of boosting iterations.
`String[]`	`getOptions()` Gets the current settings of ADTree.
`protected int`	`getRandom(int max)` Gets the next random value.
`int`	`getRandomSeed()` Gets random seed for a random walk.
`String`	`getRevision()` Returns the revision string.
`boolean`	`getSaveInstanceData()` Gets whether the tree is to save instance data.
`SelectedTag`	`getSearchPath()` Gets the method of searching the tree for a new insertion.
`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.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`graph()` Returns graph describing the tree.
`protected void`	`graphTraverse(PredictionNode currentNode, StringBuffer text, int splitOrder, int predOrder, Instances instances)` Traverses the tree, graphing each node.
`int`	`graphType()` Returns the type of graph this classifier represents.
`void`	`initClassifier(Instances instances)` Sets up the tree ready to be trained, using two-class optimized method.
`String`	`legend()` Returns the legend of the tree, describing how results are to be interpreted.
`Enumeration`	`listOptions()` Returns an enumeration describing the available options..
`static void`	`main(String[] argv)` Main method for testing this class.
`double`	`measureExamplesProcessed()` Returns the number of examples "counted".
`double`	`measureNodesExpanded()` Returns the number of nodes expanded.
`double`	`measureNumLeaves()` Calls measure function for leaf size - the number of prediction nodes.
`double`	`measureNumPredictionLeaves()` Calls measure function for prediction leaf size - the number of prediction nodes without children.
`double`	`measureTreeSize()` Calls measure function for tree size - the total number of nodes.
`void`	`merge(ADTree mergeWith)` Merges two trees together.
`void`	`next(int iteration)` Performs one iteration.
`int`	`nextSplitAddedOrder()` Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
`protected int`	`numOfAllNodes(PredictionNode root)` Returns the total number of nodes in a tree.
`String`	`numOfBoostingIterationsTipText()`
`protected int`	`numOfPredictionLeafNodes(PredictionNode root)` Returns the number of leaf nodes in a tree - prediction nodes without children.
`protected int`	`numOfPredictionNodes(PredictionNode root)` Returns the number of prediction nodes in a tree.
`protected double`	`predictionValueForInstance(Instance inst, PredictionNode currentNode, double currentValue)` Returns the class prediction value (vote) for an instance.
`String`	`randomSeedTipText()`
`String`	`saveInstanceDataTipText()`
`String`	`searchPathTipText()`
`void`	`setNumOfBoostingIterations(int b)` Sets the number of boosting iterations.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setRandomSeed(int seed)` Sets random seed for a random walk.
`void`	`setSaveInstanceData(boolean v)` Sets whether the tree is to save instance data.
`void`	`setSearchPath(SelectedTag newMethod)` Sets the method of searching the tree for a new insertion.
`String`	`toString()` Returns a description of the classifier.
`protected String`	`toString(PredictionNode currentNode, int level)` Traverses the tree, forming a string that describes it.

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

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

- Field Detail
  - SEARCHPATH_ALL
```
public static final int SEARCHPATH_ALL
```
    search mode: Expand all paths
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_HEAVIEST
```
public static final int SEARCHPATH_HEAVIEST
```
    search mode: Expand the heaviest path
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_ZPURE
```
public static final int SEARCHPATH_ZPURE
```
    search mode: Expand the best z-pure path
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_RANDOM
```
public static final int SEARCHPATH_RANDOM
```
    search mode: Expand a random path
    
    See Also:
    
    Constant Field Values
  - TAGS_SEARCHPATH
```
public static final Tag[] TAGS_SEARCHPATH
```
    The search modes
  - m_trainInstances
```
protected Instances m_trainInstances
```
    The instances used to train the tree
  - m_root
```
protected PredictionNode m_root
```
    The root of the tree
  - m_random
```
protected Random m_random
```
    The random number generator - used for the random search heuristic
  - m_lastAddedSplitNum
```
protected int m_lastAddedSplitNum
```
    The number of the last splitter added to the tree
  - m_numericAttIndices
```
protected int[] m_numericAttIndices
```
    An array containing the inidices to the numeric attributes in the data
  - m_nominalAttIndices
```
protected int[] m_nominalAttIndices
```
    An array containing the inidices to the nominal attributes in the data
  - m_trainTotalWeight
```
protected double m_trainTotalWeight
```
    The total weight of the instances - used to speed Z calculations
  - m_posTrainInstances
```
protected ReferenceInstances m_posTrainInstances
```
    The training instances with positive class - referencing the training dataset
  - m_negTrainInstances
```
protected ReferenceInstances m_negTrainInstances
```
    The training instances with negative class - referencing the training dataset
  - m_search_bestInsertionNode
```
protected PredictionNode m_search_bestInsertionNode
```
    The best node to insert under, as found so far by the latest search
  - m_search_bestSplitter
```
protected Splitter m_search_bestSplitter
```
    The best splitter to insert, as found so far by the latest search
  - m_search_smallestZ
```
protected double m_search_smallestZ
```
    The smallest Z value found so far by the latest search
  - m_search_bestPathPosInstances
```
protected Instances m_search_bestPathPosInstances
```
    The positive instances that apply to the best path found so far
  - m_search_bestPathNegInstances
```
protected Instances m_search_bestPathNegInstances
```
    The negative instances that apply to the best path found so far
  - m_nodesExpanded
```
protected int m_nodesExpanded
```
    Statistics - the number of prediction nodes investigated during search
  - m_examplesCounted
```
protected int m_examplesCounted
```
    Statistics - the number of instances processed during search
  - m_boostingIterations
```
protected int m_boostingIterations
```
    Option - the number of boosting iterations o perform
  - m_searchPath
```
protected int m_searchPath
```
    Option - the search mode
  - m_randomSeed
```
protected int m_randomSeed
```
    Option - the seed to use for a random search
  - m_saveInstanceData
```
protected boolean m_saveInstanceData
```
    Option - whether the tree should remember the instance data
- Constructor Detail
  - ADTree
```
public ADTree()
```
- Method Detail
  - globalInfo
```
public 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 Exception
```
    Sets up the tree ready to be trained, using two-class optimized method.
    
    Specified by:
    
    initClassifier in interface IterativeClassifier
    
    Parameters:
    
    instances - the instances to train the tree with
    
    Throws:
    
    Exception - if training data is unsuitable
  - next
```
public void next(int iteration)
          throws Exception
```
    Performs one iteration.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Parameters:
    
    iteration - the index of the current iteration (0-based)
    
    Throws:
    
    Exception - if this iteration fails
  - boost
```
public void boost()
           throws Exception
```
    Performs a single boosting iteration, using two-class optimized method. Will add a new splitter node and two prediction nodes to the tree (unless merging takes place).
    
    Throws:
    
    Exception - if try to boost without setting up tree first or there are no instances to train with
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
```
    Returns the class probability distribution for an instance.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution the tree generates for the instance
  - predictionValueForInstance
```
protected double predictionValueForInstance(Instance inst,
                                            PredictionNode currentNode,
                                            double currentValue)
```
    Returns the class prediction value (vote) for an instance.
    
    Parameters:
    
    inst - the instance
    
    currentNode - the root of the tree to get the values from
    
    currentValue - the current value before adding the value contained in the subtree
    
    Returns:
    
    the class prediction value (vote)
  - toString
```
public String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a string containing a description of the classifier
  - toString
```
protected String toString(PredictionNode currentNode,
                          int level)
```
    Traverses the tree, forming a string that describes it.
    
    Parameters:
    
    currentNode - the current node under investigation
    
    level - the current level in the tree
    
    Returns:
    
    the string describing the subtree
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    Drawable.TREE
  - graph
```
public String graph()
             throws Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph of the tree in dotty format
    
    Throws:
    
    Exception - if something goes wrong
  - graphTraverse
```
protected void graphTraverse(PredictionNode currentNode,
                             StringBuffer text,
                             int splitOrder,
                             int predOrder,
                             Instances instances)
                      throws Exception
```
    Traverses the tree, graphing each node.
    
    Parameters:
    
    currentNode - the currentNode under investigation
    
    text - the string built so far
    
    splitOrder - the order the parent splitter was added to the tree
    
    predOrder - the order this predictor was added to the split
    
    instances - the data to work on
    
    Throws:
    
    Exception - if something goes wrong
  - legend
```
public 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 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
  - searchPathTipText
```
public String searchPathTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSearchPath
```
public SelectedTag getSearchPath()
```
    Gets the method of searching the tree for a new insertion. Will be one of SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
    
    Returns:
    
    the tree searching mode
  - setSearchPath
```
public void setSearchPath(SelectedTag newMethod)
```
    Sets the method of searching the tree for a new insertion. Will be one of SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
    
    Parameters:
    
    newMethod - the new tree searching mode
  - randomSeedTipText
```
public String randomSeedTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRandomSeed
```
public int getRandomSeed()
```
    Gets random seed for a random walk.
    
    Returns:
    
    the random seed
  - setRandomSeed
```
public void setRandomSeed(int seed)
```
    Sets random seed for a random walk.
    
    Parameters:
    
    seed - the random seed
  - saveInstanceDataTipText
```
public String saveInstanceDataTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSaveInstanceData
```
public boolean getSaveInstanceData()
```
    Gets whether the tree is to save instance data.
    
    Returns:
    
    the random seed
  - setSaveInstanceData
```
public void setSaveInstanceData(boolean v)
```
    Sets whether the tree is to save instance data.
    
    Parameters:
    
    v - true then the tree saves instance data
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options..
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class Classifier
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options. Valid options are:
    -B num
    Set the number of boosting iterations (default 10)
    -E num
    Set the nodes to expand: -3(all), -2(weight), -1(z_pure), >=0 seed for random walk (default -3)
    -D
    Save the instance data with the model
    
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of ADTree.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - measureTreeSize
```
public double measureTreeSize()
```
    Calls measure function for tree size - the total number of nodes.
    
    Returns:
    
    the tree size
  - measureNumLeaves
```
public double measureNumLeaves()
```
    Calls measure function for leaf size - the number of prediction nodes.
    
    Returns:
    
    the leaf size
  - measureNumPredictionLeaves
```
public double measureNumPredictionLeaves()
```
    Calls measure function for prediction leaf size - the number of prediction nodes without children.
    
    Returns:
    
    the leaf size
  - measureNodesExpanded
```
public double measureNodesExpanded()
```
    Returns the number of nodes expanded.
    
    Returns:
    
    the number of nodes expanded during search
  - measureExamplesProcessed
```
public double measureExamplesProcessed()
```
    Returns the number of examples "counted".
    
    Returns:
    
    the number of nodes processed during search
  - enumerateMeasures
```
public 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(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:
    
    IllegalArgumentException - if the named measure is not supported
  - numOfAllNodes
```
protected int numOfAllNodes(PredictionNode root)
```
    Returns the total number of nodes in a tree.
    
    Parameters:
    
    root - the root of the tree being measured
    
    Returns:
    
    tree size in number of splitter + prediction nodes
  - numOfPredictionNodes
```
protected int numOfPredictionNodes(PredictionNode root)
```
    Returns the number of prediction nodes in a tree.
    
    Parameters:
    
    root - the root of the tree being measured
    
    Returns:
    
    tree size in number of prediction nodes
  - numOfPredictionLeafNodes
```
protected int numOfPredictionLeafNodes(PredictionNode root)
```
    Returns the number of leaf nodes in a tree - prediction nodes without children.
    
    Parameters:
    
    root - the root of the tree being measured
    
    Returns:
    
    tree leaf size in number of prediction nodes
  - getRandom
```
protected int getRandom(int max)
```
    Gets the next random value.
    
    Parameters:
    
    max - the maximum value (+1) to be returned
    
    Returns:
    
    the next random value (between 0 and max-1)
  - nextSplitAddedOrder
```
public int nextSplitAddedOrder()
```
    Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
    
    Returns:
    
    the next number in the order
  - 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 instances)
                     throws Exception
```
    Builds a classifier for a set of instances.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    instances - the instances to train the classifier with
    
    Throws:
    
    Exception - if something goes wrong
  - done
```
public void done()
```
    Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
    
    Specified by:
    
    done in interface IterativeClassifier
  - clone
```
public Object clone()
```
    Creates a clone that is identical to the current tree, but is independent. Deep copies the essential elements such as the tree nodes, and the instances (because the weights change.) Reference copies several elements such as the potential splitter sets, assuming that such elements should never differ between clones.
    
    Specified by:
    
    clone in interface IterativeClassifier
    
    Overrides:
    
    clone in class Object
    
    Returns:
    
    the clone
  - merge
```
public void merge(ADTree mergeWith)
           throws 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:
    
    Exception - if merge could not be performed
  - getRevision
```
public 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(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class ADTree

Field Summary

Fields inherited from class weka.classifiers.Classifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

SEARCHPATH_ALL

SEARCHPATH_HEAVIEST

SEARCHPATH_ZPURE

SEARCHPATH_RANDOM

TAGS_SEARCHPATH

m_trainInstances

m_root

m_random

m_lastAddedSplitNum

m_numericAttIndices

m_nominalAttIndices

m_trainTotalWeight

m_posTrainInstances

m_negTrainInstances

m_search_bestInsertionNode

m_search_bestSplitter

m_search_smallestZ

m_search_bestPathPosInstances

m_search_bestPathNegInstances

m_nodesExpanded

m_examplesCounted

m_boostingIterations

m_searchPath

m_randomSeed

m_saveInstanceData

Constructor Detail

ADTree

Method Detail

globalInfo

getTechnicalInformation

initClassifier

next

boost

distributionForInstance

predictionValueForInstance

toString

toString

graphType

graph

graphTraverse

legend

numOfBoostingIterationsTipText

getNumOfBoostingIterations

setNumOfBoostingIterations

searchPathTipText

getSearchPath

setSearchPath

randomSeedTipText

getRandomSeed

setRandomSeed

saveInstanceDataTipText

getSaveInstanceData

setSaveInstanceData

listOptions

setOptions

getOptions

measureTreeSize

measureNumLeaves

measureNumPredictionLeaves

measureNodesExpanded

measureExamplesProcessed

enumerateMeasures

getMeasure

numOfAllNodes

numOfPredictionNodes

numOfPredictionLeafNodes

getRandom

nextSplitAddedOrder

getCapabilities

buildClassifier

done