HoeffdingTree (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.HoeffdingTree

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class HoeffdingTree
extends AbstractClassifier
implements UpdateableClassifier, WeightedInstancesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, Drawable, java.io.Serializable
```
A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).

A theoretically appealing feature of Hoeffding Trees not shared by otherincremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. For more information see:

Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
BibTeX:
```
 @inproceedings{Hulten2001,
    author = {Geoff Hulten and Laurie Spencer and Pedro Domingos},
    booktitle = {ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining},
    pages = {97-106},
    publisher = {ACM Press},
    title = {Mining time-changing data streams},
    year = {2001}
 }
 
```
Valid options are:
```
 -L
  The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive.
  (default = 0)
 
```
```
 -S
  The splitting criterion to use. 0 = Gini, 1 = Info gain
  (default = 0)
 
```
```
 -E
  The allowable error in a split decision - values closer to zero will take longer to decide
  (default = 1e-7)
 
```
```
 -H
  Threshold below which a split will be forced to break ties
  (default = 0.05)
 
```
```
 -M
  Minimum fraction of weight required down at least two branches for info gain splitting
  (default = 0.01)
 
```
```
 -G
  Grace period - the number of instances a leaf should observe between split attempts
  (default = 200)
 
```
```
 -N
  The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only)
  (default = 0)
 
```
```
 -P
  Print leaf models when using naive Bayes at the leaves.
 
```
See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`GINI_SPLIT`
`static int`	`INFO_GAIN_SPLIT`
`static int`	`LEAF_MAJ_CLASS`
`static int`	`LEAF_NB`
`static int`	`LEAF_NB_ADAPTIVE`
`protected int`	`m_activeLeafCount`
`protected int`	`m_decisionNodeCount`
`protected double`	`m_gracePeriod` The number of instances a leaf should observe between split attempts
`protected Instances`	`m_header`
`protected double`	`m_hoeffdingTieThreshold` Threshold below which a split will be forced to break ties
`protected int`	`m_inactiveLeafCount`
`protected int`	`m_leafStrategy` The leaf prediction strategy to use
`protected double`	`m_minFracWeightForTwoBranchesGain` The minimum fraction of weight required down at least two branches for info gain splitting
`protected double`	`m_nbThreshold` The number of instances (total weight) a leaf should observe before allowing naive Bayes to make predictions
`protected boolean`	`m_printLeafModels` Print out leaf models in the case of naive Bayes or naive Bayes adaptive leaves
`protected HNode`	`m_root`
`protected int`	`m_selectedSplitMetric` The splitting metric to use
`protected double`	`m_splitConfidence` The allowable error in a split decision.
`protected SplitMetric`	`m_splitMetric`
`static Tag[]`	`TAGS_SELECTION`
`static Tag[]`	`TAGS_SELECTION2`

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

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

Constructor Summary

Constructors
Constructor and Description

HoeffdingTree()

Constructors
Constructor and Description
`HoeffdingTree()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`activateNode(InactiveHNode toActivate, SplitNode parent, java.lang.String parentBranch)` Activate (allow growth) the supplied node
`void`	`buildClassifier(Instances data)` Builds the classifier.
`protected static double`	`computeHoeffdingBound(double max, double confidence, double weight)`
`protected void`	`deactivateNode(ActiveHNode toDeactivate, SplitNode parent, java.lang.String parentBranch)` Deactivate (prevent growth) from the supplied node
`double[]`	`distributionForInstance(Instance inst)` Returns class probabilities for an instance.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getGracePeriod()` Get the number of instances (or total weight of instances) a leaf should observe between split attempts
`double`	`getHoeffdingTieThreshold()` Get the threshold below which a split will be forced to break ties
`SelectedTag`	`getLeafPredictionStrategy()` Get the leaf prediction strategy to use (majority class, naive Bayes or naive Bayes adaptive)
`double`	`getMinimumFractionOfWeightInfoGain()` Get the minimum fraction of weight required down at least two branches for info gain splitting
`double`	`getNaiveBayesPredictionThreshold()` Get the number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`boolean`	`getPrintLeafModels()`
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSplitConfidence()` Get the allowable error in a split decision.
`SelectedTag`	`getSplitCriterion()` Get the split criterion to use (either Gini or info gain).
`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`	`gracePeriodTipText()` Returns the tip text for this property
`java.lang.String`	`graph()` Returns a string that describes a graph representing the object.
`int`	`graphType()` Returns the type of graph representing the object.
`java.lang.String`	`hoeffdingTieThresholdTipText()` Returns the tip text for this property
`java.lang.String`	`leafPredictionStrategyTipText()` Returns the tip text for this property
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)`
`java.lang.String`	`minimumFractionOfWeightInfoGainTipText()` Returns the tip text for this property
`java.lang.String`	`naiveBayesPredictionThresholdTipText()` Returns the tip text for this property
`protected ActiveHNode`	`newLearningNode()` Create a new learning node (either majority class, naive Bayes or naive Bayes adaptive)
`java.lang.String`	`printLeafModelsTipText()` Returns the tip text for this property
`protected void`	`reset()`
`void`	`setGracePeriod(double grace)` Set the number of instances (or total weight of instances) a leaf should observe between split attempts
`void`	`setHoeffdingTieThreshold(double ht)` Set the threshold below which a split will be forced to break ties
`void`	`setLeafPredictionStrategy(SelectedTag strat)` Set the leaf prediction strategy to use (majority class, naive Bayes or naive Bayes adaptive)
`void`	`setMinimumFractionOfWeightInfoGain(double m)` Set the minimum fraction of weight required down at least two branches for info gain splitting
`void`	`setNaiveBayesPredictionThreshold(double n)` Set the number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPrintLeafModels(boolean p)`
`void`	`setSplitConfidence(double sc)` Set the allowable error in a split decision.
`void`	`setSplitCriterion(SelectedTag crit)` Set the split criterion to use (either Gini or info gain).
`java.lang.String`	`splitConfidenceTipText()` Returns the tip text for this property
`java.lang.String`	`splitCriterionTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Return a textual description of the mode
`protected void`	`trySplit(ActiveHNode node, SplitNode parent, java.lang.String parentBranch)` Try a split from the supplied node
`void`	`updateClassifier(Instance inst)` Updates the classifier with the given instance.

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

- Field Detail
  - m_header
```
protected Instances m_header
```
  - m_root
```
protected HNode m_root
```
  - m_gracePeriod
```
protected double m_gracePeriod
```
    The number of instances a leaf should observe between split attempts
  - m_splitConfidence
```
protected double m_splitConfidence
```
    The allowable error in a split decision. Values closer to zero will take longer to decide
  - m_hoeffdingTieThreshold
```
protected double m_hoeffdingTieThreshold
```
    Threshold below which a split will be forced to break ties
  - m_minFracWeightForTwoBranchesGain
```
protected double m_minFracWeightForTwoBranchesGain
```
    The minimum fraction of weight required down at least two branches for info gain splitting
  - m_selectedSplitMetric
```
protected int m_selectedSplitMetric
```
    The splitting metric to use
  - m_splitMetric
```
protected SplitMetric m_splitMetric
```
  - m_leafStrategy
```
protected int m_leafStrategy
```
    The leaf prediction strategy to use
  - m_nbThreshold
```
protected double m_nbThreshold
```
    The number of instances (total weight) a leaf should observe before allowing naive Bayes to make predictions
  - m_activeLeafCount
```
protected int m_activeLeafCount
```
  - m_inactiveLeafCount
```
protected int m_inactiveLeafCount
```
  - m_decisionNodeCount
```
protected int m_decisionNodeCount
```
  - GINI_SPLIT
```
public static final int GINI_SPLIT
```
    See Also:
    
    Constant Field Values
  - INFO_GAIN_SPLIT
```
public static final int INFO_GAIN_SPLIT
```
    See Also:
    
    Constant Field Values
  - TAGS_SELECTION
```
public static final Tag[] TAGS_SELECTION
```
  - LEAF_MAJ_CLASS
```
public static final int LEAF_MAJ_CLASS
```
    See Also:
    
    Constant Field Values
  - LEAF_NB
```
public static final int LEAF_NB
```
    See Also:
    
    Constant Field Values
  - LEAF_NB_ADAPTIVE
```
public static final int LEAF_NB_ADAPTIVE
```
    See Also:
    
    Constant Field Values
  - TAGS_SELECTION2
```
public static final Tag[] TAGS_SELECTION2
```
  - m_printLeafModels
```
protected boolean m_printLeafModels
```
    Print out leaf models in the case of naive Bayes or naive Bayes adaptive leaves
- Constructor Detail
  - HoeffdingTree
```
public HoeffdingTree()
```
- 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
  - reset
```
protected void reset()
```
  - 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
  - listOptions
```
public java.util.Enumeration<Option> 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:
```
 -L
  The leaf prediction strategy to use. 0 = majority class, 1 = naive Bayes, 2 = naive Bayes adaptive.
  (default = 0)
 
```
```
 -S
  The splitting criterion to use. 0 = Gini, 1 = Info gain
  (default = 0)
 
```
```
 -E
  The allowable error in a split decision - values closer to zero will take longer to decide
  (default = 1e-7)
 
```
```
 -H
  Threshold below which a split will be forced to break ties
  (default = 0.05)
 
```
```
 -M
  Minimum fraction of weight required down at least two branches for info gain splitting
  (default = 0.01)
 
```
```
 -G
  Grace period - the number of instances a leaf should observe between split attempts
  (default = 200)
 
```
```
 -N
  The number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions (NB or NB adaptive only)
  (default = 0)
 
```
```
 -P
  Print leaf models when using naive Bayes at the leaves.
 
```
    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 the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - printLeafModelsTipText
```
public java.lang.String printLeafModelsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPrintLeafModels
```
public void setPrintLeafModels(boolean p)
```
  - getPrintLeafModels
```
public boolean getPrintLeafModels()
```
  - minimumFractionOfWeightInfoGainTipText
```
public java.lang.String minimumFractionOfWeightInfoGainTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setMinimumFractionOfWeightInfoGain
```
public void setMinimumFractionOfWeightInfoGain(double m)
```
    Set the minimum fraction of weight required down at least two branches for info gain splitting
    
    Parameters:
    
    m - the minimum fraction of weight
  - getMinimumFractionOfWeightInfoGain
```
public double getMinimumFractionOfWeightInfoGain()
```
    Get the minimum fraction of weight required down at least two branches for info gain splitting
    
    Returns:
    
    the minimum fraction of weight
  - gracePeriodTipText
```
public java.lang.String gracePeriodTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setGracePeriod
```
public void setGracePeriod(double grace)
```
    Set the number of instances (or total weight of instances) a leaf should observe between split attempts
    
    Parameters:
    
    grace - the grace period
  - getGracePeriod
```
public double getGracePeriod()
```
    Get the number of instances (or total weight of instances) a leaf should observe between split attempts
    
    Returns:
    
    the grace period
  - hoeffdingTieThresholdTipText
```
public java.lang.String hoeffdingTieThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setHoeffdingTieThreshold
```
public void setHoeffdingTieThreshold(double ht)
```
    Set the threshold below which a split will be forced to break ties
    
    Parameters:
    
    ht - the threshold
  - getHoeffdingTieThreshold
```
public double getHoeffdingTieThreshold()
```
    Get the threshold below which a split will be forced to break ties
    
    Returns:
    
    the threshold
  - splitConfidenceTipText
```
public java.lang.String splitConfidenceTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSplitConfidence
```
public void setSplitConfidence(double sc)
```
    Set the allowable error in a split decision. Values closer to zero will take longer to decide.
    
    Parameters:
    
    sc - the split confidence
  - getSplitConfidence
```
public double getSplitConfidence()
```
    Get the allowable error in a split decision. Values closer to zero will take longer to decide.
    
    Returns:
    
    the split confidence
  - splitCriterionTipText
```
public java.lang.String splitCriterionTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSplitCriterion
```
public void setSplitCriterion(SelectedTag crit)
```
    Set the split criterion to use (either Gini or info gain).
    
    Parameters:
    
    crit - the criterion to use
  - getSplitCriterion
```
public SelectedTag getSplitCriterion()
```
    Get the split criterion to use (either Gini or info gain).
    
    Returns:
    
    the criterion to use
  - leafPredictionStrategyTipText
```
public java.lang.String leafPredictionStrategyTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setLeafPredictionStrategy
```
public void setLeafPredictionStrategy(SelectedTag strat)
```
    Set the leaf prediction strategy to use (majority class, naive Bayes or naive Bayes adaptive)
    
    Parameters:
    
    strat - the strategy to use
  - getLeafPredictionStrategy
```
public SelectedTag getLeafPredictionStrategy()
```
    Get the leaf prediction strategy to use (majority class, naive Bayes or naive Bayes adaptive)
    
    Returns:
    
    the strategy to use
  - naiveBayesPredictionThresholdTipText
```
public java.lang.String naiveBayesPredictionThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNaiveBayesPredictionThreshold
```
public void setNaiveBayesPredictionThreshold(double n)
```
    Set the number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions
    
    Parameters:
    
    n - the number/weight of instances
  - getNaiveBayesPredictionThreshold
```
public double getNaiveBayesPredictionThreshold()
```
    Get the number of instances (weight) a leaf should observe before allowing naive Bayes to make predictions
    
    Returns:
    
    the number/weight of instances
  - computeHoeffdingBound
```
protected static double computeHoeffdingBound(double max,
                                              double confidence,
                                              double weight)
```
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Builds the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully
  - updateClassifier
```
public void updateClassifier(Instance inst)
                      throws java.lang.Exception
```
    Updates the classifier with the given instance.
    
    Specified by:
    
    updateClassifier in interface UpdateableClassifier
    
    Parameters:
    
    instance - the new training instance to include in the model
    
    Throws:
    
    java.lang.Exception - if the instance could not be incorporated in the model.
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Returns class probabilities for an instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to compute the distribution for
    
    Returns:
    
    the class probabilities
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed successfully
  - deactivateNode
```
protected void deactivateNode(ActiveHNode toDeactivate,
                              SplitNode parent,
                              java.lang.String parentBranch)
```
    Deactivate (prevent growth) from the supplied node
    
    Parameters:
    
    toDeactivate - the node to deactivate
    
    parent - the node's parent
    
    parentBranch - the branch leading to the node
  - activateNode
```
protected void activateNode(InactiveHNode toActivate,
                            SplitNode parent,
                            java.lang.String parentBranch)
```
    Activate (allow growth) the supplied node
    
    Parameters:
    
    toActivate - the node to activate
    
    parent - the node's parent
    
    parentBranch - the branch leading to the node
  - trySplit
```
protected void trySplit(ActiveHNode node,
                        SplitNode parent,
                        java.lang.String parentBranch)
                 throws java.lang.Exception
```
    Try a split from the supplied node
    
    Parameters:
    
    node - the node to split
    
    parent - the parent of the node
    
    parentBranch - the branch leading to the node
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - newLearningNode
```
protected ActiveHNode newLearningNode()
                               throws java.lang.Exception
```
    Create a new learning node (either majority class, naive Bayes or naive Bayes adaptive)
    
    Returns:
    
    a new learning node
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - toString
```
public java.lang.String toString()
```
    Return a textual description of the mode
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a String describing the model
  - 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[] args)
```
  - graphType
```
public int graphType()
```
    Description copied from interface: Drawable
    
    Returns the type of graph representing the object.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    the type of graph representing the object
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Description copied from interface: Drawable
    
    Returns a string that describes a graph representing the object. The string should be in XMLBIF ver. 0.3 format if the graph is a BayesNet, otherwise it should be in dotty format.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph described by a string
    
    Throws:
    
    java.lang.Exception - if the graph can't be computed

Class HoeffdingTree

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_header

m_root

m_gracePeriod

m_splitConfidence

m_hoeffdingTieThreshold

m_minFracWeightForTwoBranchesGain

m_selectedSplitMetric

m_splitMetric

m_leafStrategy

m_nbThreshold

m_activeLeafCount

m_inactiveLeafCount

m_decisionNodeCount

GINI_SPLIT

INFO_GAIN_SPLIT

TAGS_SELECTION

LEAF_MAJ_CLASS

LEAF_NB

LEAF_NB_ADAPTIVE

TAGS_SELECTION2

m_printLeafModels

Constructor Detail

HoeffdingTree

Method Detail

globalInfo

getTechnicalInformation

reset

getCapabilities

listOptions

setOptions

getOptions

printLeafModelsTipText

setPrintLeafModels

getPrintLeafModels

minimumFractionOfWeightInfoGainTipText

setMinimumFractionOfWeightInfoGain

getMinimumFractionOfWeightInfoGain

gracePeriodTipText

setGracePeriod

getGracePeriod

hoeffdingTieThresholdTipText

setHoeffdingTieThreshold

getHoeffdingTieThreshold

splitConfidenceTipText

setSplitConfidence

getSplitConfidence

splitCriterionTipText

setSplitCriterion

getSplitCriterion

leafPredictionStrategyTipText

setLeafPredictionStrategy

getLeafPredictionStrategy

naiveBayesPredictionThresholdTipText

setNaiveBayesPredictionThreshold

getNaiveBayesPredictionThreshold

computeHoeffdingBound

buildClassifier

updateClassifier

distributionForInstance

deactivateNode

activateNode

trySplit

newLearningNode

toString

getRevision

main

graphType

graph