java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.bayes.BayesianLogisticRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
```
public class BayesianLogisticRegression
extends Classifier
implements OptionHandler, TechnicalInformationHandler
```
Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.

For more information, see

Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization. URL http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.pdf.
BibTeX:
```
 @techreport{Genkin2004,
    author = {Alexander Genkin and David D. Lewis and David Madigan},
    institution = {DIMACS},
    title = {Large-scale bayesian logistic regression for text categorization},
    year = {2004},
    URL = {http://www.stat.rutgers.edu/\~madigan/PAPERS/shortFat-v3a.pdf}
 }
 
```
Version:

$Revision: 7984 $

Author:

Navendu Garg (gargnav at iit dot edu)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`double[]`	`BetaVector`	Array for storing coefficients of Bayesian regression model.
`double`	`Change`	This variable is used to keep track of change in the value of delta summation of r(i).
`int`	`ClassIndex`	The class index from the training data
`static int`	`CV_BASED`
`double[]`	`Delta`	Trust Region Radius
`double[]`	`DeltaBeta`	Array to store Regression Coefficient updates.
`double[]`	`DeltaR`	This vector is used to store the increments on the R(i).
`double[]`	`DeltaUpdate`	Trust Region Radius Update
`static int`	`GAUSSIAN`	Distributions available
`java.lang.String`	`HyperparameterRange`	CV Hyperparameter Range
`double[]`	`Hyperparameters`	Array to store Hyperparameter values for each feature.
`int`	`HyperparameterSelection`	Hyperparameter selection method
`double`	`HyperparameterValue`	Best hyperparameter for test phase
`static double[]`	`InputHyperparameterValues`	Set of values to be used as hyperparameter values during Cross-Validation.
`int`	`iterationCounter`	Iteration counter
`static int`	`LAPLACIAN`
`static double[]`	`LogLikelihood`	Log-likelihood values to be used to choose the best hyperparameter.
`Filter`	`m_Filter`	Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
`int`	`m_seed`	seed for randomizing the instances before CV
`int`	`maxIterations`	Maximum number of iterations
`static int`	`NORM_BASED`	Methods for selecting the hyperparameter value
`boolean`	`NormalizeData`	Choose whether to normalize data or not
`int`	`NumFolds`	NumFolds for CV based Hyperparameters selection
`int`	`PriorClass`	Distribution Prior class
`double[]`	`R`	R(i)= BetaVector X x(i) X y(i).
`static int`	`SPECIFIC_VALUE`
`static Tag[]`	`TAGS_HYPER_METHOD`
`static Tag[]`	`TAGS_PRIOR`
`double`	`Threshold`	Threshold for binary classification of probabilisitic estimate
`double`	`Tolerance`	Tolerance criteria for the stopping criterion.

Constructor Summary

Constructors
Constructor Description

BayesianLogisticRegression()

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static double`	`bigF(double r, double sigma)`	This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region.
`void`	`buildClassifier(Instances data)`	(1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.
`double`	`classifyInstance(Instance instance)`	Classifies the given instance using the Bayesian Logistic Regression function.
`static double`	`classSgn(double value)`	This class is used to mask the internal class labels.
`double`	`CVBasedHyperparameter()`	Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood.
`java.lang.String`	`debugTipText()`	Returns the tip text for this property
`Capabilities`	`getCapabilities()`	This method tests what kind of data this classifier can handle.
`java.lang.String`	`getHyperparameterRange()`	Get the range of hyperparameter values to consider during CV-based selection.
`SelectedTag`	`getHyperparameterSelection()`	Get the method used to select the hyperparameter
`double`	`getHyperparameterValue()`	Get the hyperparameter value.
`double`	`getLoglikeliHood(double[] betas, Instances instances)`
`int`	`getMaxIterations()`	Get the maximum number of iterations to perform
`int`	`getNumFolds()`	Return the number of folds for CV-based hyperparameter selection
`java.lang.String[]`	`getOptions()`	Gets the current settings of the Classifier.
`SelectedTag`	`getPriorClass()`	Get the type of prior to use.
`java.lang.String`	`getRevision()`	Returns the revision string.
`int`	`getSeed()`	Get the seed for randomizing the instances for CV-based hyperparameter selection
`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.
`double`	`getThreshold()`	Return the threshold being used.
`double`	`getTolerance()`	Get the tolerance value
`java.lang.String`	`globalInfo()`
`java.lang.String`	`hyperparameterRangeTipText()`	Returns the tip text for this property
`java.lang.String`	`hyperparameterSelectionTipText()`	Returns the tip text for this property
`java.lang.String`	`hyperparameterValueTipText()`	Returns the tip text for this property
`void`	`initialize()`	(1)Initialize m_Beta[j] to 0.
`boolean`	`isDebug()`	Returns true if debug is turned on.
`boolean`	`isNormalizeData()`	Returns true if the data is to be normalized first
`java.util.Enumeration`	`listOptions()`	Returns an enumeration describing the available options.
`static double`	`logisticLinkFunction(double r)`	This method computes the values for the logistic link function.
`static void`	`main(java.lang.String[] argv)`	Main method for testing this class.
`java.lang.String`	`maxIterationsTipText()`	Returns the tip text for this property
`java.lang.String`	`normalizeDataTipText()`	Returns the tip text for this property
`double`	`normBasedHyperParameter()`	This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
`java.lang.String`	`numFoldsTipText()`	Returns the tip text for this property
`java.lang.String`	`priorClassTipText()`	Returns the tip text for this property
`java.lang.String`	`seedTipText()`	Returns the tip text for this property
`void`	`setDebug(boolean debugMode)`	Set debugging mode.
`void`	`setHyperparameterRange(java.lang.String hyperparameterRange)`	Set the range of hyperparameter values to consider during CV-based selection
`void`	`setHyperparameterSelection(SelectedTag newMethod)`	Set the method used to select the hyperparameter
`void`	`setHyperparameterValue(double hyperparameterValue)`	Set the hyperparameter value.
`void`	`setMaxIterations(int maxIterations)`	Set the maximum number of iterations to perform
`void`	`setNormalizeData(boolean normalizeData)`	Set whether to normalize the data or not
`void`	`setNumFolds(int numFolds)`	Set the number of folds to use for CV-based hyperparameter selection
`void`	`setOptions(java.lang.String[] options)`	Parses a given list of options.
`void`	`setPriorClass(SelectedTag newMethod)`	Set the type of prior to use.
`void`	`setSeed(int seed)`	Set the seed for randomizing the instances for CV-based hyperparameter selection
`void`	`setThreshold(double threshold)`	Set the threshold to use.
`void`	`setTolerance(double tolerance)`	Set the tolerance value
`static double`	`sgn(double r)`	Sign for a given value.
`boolean`	`stoppingCriterion()`	This method implements the stopping criterion function.
`java.lang.String`	`thresholdTipText()`	Returns the tip text for this property
`java.lang.String`	`toleranceTipText()`	Returns the tip text for this property
`java.lang.String`	`toString()`	Outputs the linear regression model as a string.

Methods inherited from class weka.classifiers.Classifier
distributionForInstance, forName, getDebug, makeCopies, makeCopy

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

- Field Detail
  - LogLikelihood
```
public static double[] LogLikelihood
```
    Log-likelihood values to be used to choose the best hyperparameter.
  - InputHyperparameterValues
```
public static double[] InputHyperparameterValues
```
    Set of values to be used as hyperparameter values during Cross-Validation.
  - NormalizeData
```
public boolean NormalizeData
```
    Choose whether to normalize data or not
  - Tolerance
```
public double Tolerance
```
    Tolerance criteria for the stopping criterion.
  - Threshold
```
public double Threshold
```
    Threshold for binary classification of probabilisitic estimate
  - GAUSSIAN
```
public static final int GAUSSIAN
```
    Distributions available
    
    See Also:
    
    Constant Field Values
  - LAPLACIAN
```
public static final int LAPLACIAN
```
    See Also:
    
    Constant Field Values
  - TAGS_PRIOR
```
public static final Tag[] TAGS_PRIOR
```
  - PriorClass
```
public int PriorClass
```
    Distribution Prior class
  - NumFolds
```
public int NumFolds
```
    NumFolds for CV based Hyperparameters selection
  - m_seed
```
public int m_seed
```
    seed for randomizing the instances before CV
  - NORM_BASED
```
public static final int NORM_BASED
```
    Methods for selecting the hyperparameter value
    
    See Also:
    
    Constant Field Values
  - CV_BASED
```
public static final int CV_BASED
```
    See Also:
    
    Constant Field Values
  - SPECIFIC_VALUE
```
public static final int SPECIFIC_VALUE
```
    See Also:
    
    Constant Field Values
  - TAGS_HYPER_METHOD
```
public static final Tag[] TAGS_HYPER_METHOD
```
  - HyperparameterSelection
```
public int HyperparameterSelection
```
    Hyperparameter selection method
  - ClassIndex
```
public int ClassIndex
```
    The class index from the training data
  - HyperparameterValue
```
public double HyperparameterValue
```
    Best hyperparameter for test phase
  - HyperparameterRange
```
public java.lang.String HyperparameterRange
```
    CV Hyperparameter Range
  - maxIterations
```
public int maxIterations
```
    Maximum number of iterations
  - iterationCounter
```
public int iterationCounter
```
    Iteration counter
  - BetaVector
```
public double[] BetaVector
```
    Array for storing coefficients of Bayesian regression model.
  - DeltaBeta
```
public double[] DeltaBeta
```
    Array to store Regression Coefficient updates.
  - DeltaUpdate
```
public double[] DeltaUpdate
```
    Trust Region Radius Update
  - Delta
```
public double[] Delta
```
    Trust Region Radius
  - Hyperparameters
```
public double[] Hyperparameters
```
    Array to store Hyperparameter values for each feature.
  - R
```
public double[] R
```
    R(i)= BetaVector X x(i) X y(i). This an intermediate value with respect to vector BETA, input values and corresponding class labels
  - DeltaR
```
public double[] DeltaR
```
    This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion.
  - Change
```
public double Change
```
    This variable is used to keep track of change in the value of delta summation of r(i).
  - m_Filter
```
public Filter m_Filter
```
    Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
- Constructor Detail
  - BayesianLogisticRegression
```
public BayesianLogisticRegression()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
  - initialize
```
public void initialize()
                throws java.lang.Exception
```
    (1)Initialize m_Beta[j] to 0. (2)Initialize m_DeltaUpdate[j].
    Throws:
    
    java.lang.Exception
  - getCapabilities
```
public Capabilities getCapabilities()
```
    This method tests what kind of data this classifier can handle. return Capabilities
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    (1) Set the data to the class attribute m_Instances.
    
    (2)Call the method initialize() to initialize the values.
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    
    data - training data
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully.
  - classSgn
```
public static double classSgn(double value)
```
    This class is used to mask the internal class labels.
    Parameters:
    
    value - internal class label
    
    Returns:
    
    -1 for internal class label 0
    
    +1 for internal class label 1
  - 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
  - bigF
```
public static double bigF(double r,
                          double sigma)
```
    This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region. r BetaVector X x(i)y(i). delta A parameter where sigma > 0
    
    Returns:
    
    double function value
  - stoppingCriterion
```
public boolean stoppingCriterion()
```
    This method implements the stopping criterion function.
    
    Returns:
    
    boolean whether to stop or not.
  - logisticLinkFunction
```
public static double logisticLinkFunction(double r)
```
    This method computes the values for the logistic link function.
    f(r)=exp(r)/(1+exp(r))
    Returns:
    
    output value
  - sgn
```
public static double sgn(double r)
```
    Sign for a given value.
    
    Parameters:
    
    r -
    
    Returns:
    
    double +1 if r>0, -1 if r<0
  - normBasedHyperParameter
```
public double normBasedHyperParameter()
```
    This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
  - classifyInstance
```
public double classifyInstance(Instance instance)
                        throws java.lang.Exception
```
    Classifies the given instance using the Bayesian Logistic Regression function.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    instance - the test instance
    
    Returns:
    
    the classification
    
    Throws:
    
    java.lang.Exception - if classification can't be done successfully
  - toString
```
public java.lang.String toString()
```
    Outputs the linear regression model as a string.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    the model as string
  - CVBasedHyperparameter
```
public double CVBasedHyperparameter()
                             throws java.lang.Exception
```
    Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood. The method can parse a range of values or a list of values.
    
    Returns:
    
    Best hyperparameter value with the max likelihood value on the training data.
    
    Throws:
    
    java.lang.Exception
  - getLoglikeliHood
```
public double getLoglikeliHood(double[] betas,
                               Instances instances)
```
    Returns:
    
    likelihood for a given set of betas and instances
  - listOptions
```
public java.util.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(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
    
    -D Show Debugging Output
    
    -P <integer> Distribution of the Prior (1=Gaussian, 2=Laplacian) (default: 1=Gaussian)
    
    -H <integer> Hyperparameter Selection Method (1=Norm-based, 2=CV-based, 3=specific value) (default: 1=Norm-based)
    
    -V <double> Specified Hyperparameter Value (use in conjunction with -H 3) (default: 0.27)
    
    -R <string> Hyperparameter Range (use in conjunction with -H 2) (format: R:start-end,multiplier OR L:val(1), val(2), ..., val(n)) (default: R:0.01-316,3.16)
    
    -Tl <double> Tolerance Value (default: 0.0005)
    
    -S <double> Threshold Value (default: 0.5)
    
    -F <integer> Number Of Folds (use in conjuction with -H 2) (default: 2)
    
    -I <integer> Max Number of Iterations (default: 100)
    
    -N Normalize the data
    
    -seed <number> Seed for randomizing instances order in CV-based hyperparameter selection (default: 1)
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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()
```
    Description copied from class: Classifier
    
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options
  - debugTipText
```
public java.lang.String debugTipText()
```
    Returns the tip text for this property
    
    Overrides:
    
    debugTipText in class Classifier
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebug
```
public void setDebug(boolean debugMode)
```
    Description copied from class: Classifier
    
    Set debugging mode.
    
    Overrides:
    
    setDebug in class Classifier
    
    Parameters:
    
    debugMode - true if debug output should be printed
  - hyperparameterSelectionTipText
```
public java.lang.String hyperparameterSelectionTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getHyperparameterSelection
```
public SelectedTag getHyperparameterSelection()
```
    Get the method used to select the hyperparameter
    
    Returns:
    
    the method used to select the hyperparameter
  - setHyperparameterSelection
```
public void setHyperparameterSelection(SelectedTag newMethod)
```
    Set the method used to select the hyperparameter
    
    Parameters:
    
    newMethod - the method used to set the hyperparameter
  - priorClassTipText
```
public java.lang.String priorClassTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPriorClass
```
public void setPriorClass(SelectedTag newMethod)
```
    Set the type of prior to use.
    
    Parameters:
    
    newMethod - the type of prior to use.
  - getPriorClass
```
public SelectedTag getPriorClass()
```
    Get the type of prior to use.
    
    Returns:
    
    the type of prior to use
  - thresholdTipText
```
public java.lang.String thresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getThreshold
```
public double getThreshold()
```
    Return the threshold being used.
    
    Returns:
    
    the threshold
  - setThreshold
```
public void setThreshold(double threshold)
```
    Set the threshold to use.
    
    Parameters:
    
    threshold - the threshold to use
  - toleranceTipText
```
public java.lang.String toleranceTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getTolerance
```
public double getTolerance()
```
    Get the tolerance value
    
    Returns:
    
    the tolerance value
  - setTolerance
```
public void setTolerance(double tolerance)
```
    Set the tolerance value
    
    Parameters:
    
    tolerance - the tolerance value to use
  - hyperparameterValueTipText
```
public java.lang.String hyperparameterValueTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getHyperparameterValue
```
public double getHyperparameterValue()
```
    Get the hyperparameter value. Used when the hyperparameter selection method is set to specific value
    
    Returns:
    
    the hyperparameter value
  - setHyperparameterValue
```
public void setHyperparameterValue(double hyperparameterValue)
```
    Set the hyperparameter value. Used when the hyperparameter selection method is set to specific value
    
    Parameters:
    
    hyperparameterValue - the value of the hyperparameter
  - 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()
```
    Return the number of folds for CV-based hyperparameter selection
    
    Returns:
    
    the number of CV folds
  - setNumFolds
```
public void setNumFolds(int numFolds)
```
    Set the number of folds to use for CV-based hyperparameter selection
    
    Parameters:
    
    numFolds - number of folds to select
  - seedTipText
```
public java.lang.String seedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setSeed
```
public void setSeed(int seed)
```
    Set the seed for randomizing the instances for CV-based hyperparameter selection
    
    Parameters:
    
    seed - the seed to use
  - getSeed
```
public int getSeed()
```
    Get the seed for randomizing the instances for CV-based hyperparameter selection
    
    Returns:
    
    the seed to use
  - maxIterationsTipText
```
public java.lang.String maxIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMaxIterations
```
public int getMaxIterations()
```
    Get the maximum number of iterations to perform
    
    Returns:
    
    the maximum number of iterations
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Set the maximum number of iterations to perform
    
    Parameters:
    
    maxIterations - maximum number of iterations
  - normalizeDataTipText
```
public java.lang.String normalizeDataTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - isNormalizeData
```
public boolean isNormalizeData()
```
    Returns true if the data is to be normalized first
    
    Returns:
    
    true if the data is to be normalized
  - setNormalizeData
```
public void setNormalizeData(boolean normalizeData)
```
    Set whether to normalize the data or not
    
    Parameters:
    
    normalizeData - true if data is to be normalized
  - hyperparameterRangeTipText
```
public java.lang.String hyperparameterRangeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getHyperparameterRange
```
public java.lang.String getHyperparameterRange()
```
    Get the range of hyperparameter values to consider during CV-based selection.
    
    Returns:
    
    the range of hyperparameters as a Stringe
  - setHyperparameterRange
```
public void setHyperparameterRange(java.lang.String hyperparameterRange)
```
    Set the range of hyperparameter values to consider during CV-based selection
    
    Parameters:
    
    hyperparameterRange - the range of hyperparameter values
  - isDebug
```
public boolean isDebug()
```
    Returns true if debug is turned on.
    
    Returns:
    
    true if debug is turned on
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision

Class BayesianLogisticRegression

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

LogLikelihood

InputHyperparameterValues

NormalizeData

Tolerance

Threshold

GAUSSIAN

LAPLACIAN

TAGS_PRIOR

PriorClass

NumFolds

m_seed

NORM_BASED

CV_BASED

SPECIFIC_VALUE

TAGS_HYPER_METHOD

HyperparameterSelection

ClassIndex

HyperparameterValue

HyperparameterRange

maxIterations

iterationCounter

BetaVector

DeltaBeta

DeltaUpdate

Delta

Hyperparameters

R

DeltaR

Change

m_Filter

Constructor Detail

BayesianLogisticRegression

Method Detail

globalInfo

initialize

getCapabilities

buildClassifier

classSgn

getTechnicalInformation

bigF

stoppingCriterion

logisticLinkFunction

sgn

normBasedHyperParameter

classifyInstance

toString

CVBasedHyperparameter

getLoglikeliHood

listOptions

setOptions

getOptions

main

debugTipText

setDebug

hyperparameterSelectionTipText

getHyperparameterSelection

setHyperparameterSelection

priorClassTipText

setPriorClass

getPriorClass

thresholdTipText

getThreshold

setThreshold

toleranceTipText

getTolerance

setTolerance

hyperparameterValueTipText

getHyperparameterValue

setHyperparameterValue

numFoldsTipText

getNumFolds

setNumFolds

seedTipText