LogitBoost (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
      - weka.classifiers.meta.LogitBoost

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, Sourcable, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class LogitBoost
extends RandomizableIteratedSingleClassifierEnhancer
implements Sourcable, WeightedInstancesHandler, TechnicalInformationHandler, IterativeClassifier, BatchPredictor

Class for performing additive logistic regression.
This class performs classification using a regression scheme as the base learner, and can handle multi-class problems. For more information, see

J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

BibTeX:

 @techreport{Friedman1998,
    address = {Stanford University},
    author = {J. Friedman and T. Hastie and R. Tibshirani},
    title = {Additive Logistic Regression: a Statistical View of Boosting},
    year = {1998},
    PS = {http://www-stat.stanford.edu/\~jhf/ftp/boost.ps}
 }

Valid options are:

 -Q
  Use resampling instead of reweighting for boosting.

 -use-estimated-priors
  Use estimated priors rather than uniform ones.

 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)

 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)

 -H <num>
  Shrinkage parameter.
  (default 1)

 -Z <num>
  Z max threshold for responses.
  (default 3)

 -O <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)

 -E <int>
  The number of threads to use for batch prediction, which should be >= size of thread pool.
  (default 1)

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 Options specific to classifier weka.classifiers.trees.DecisionStump:

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

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

Options after -- are passed to the designated learner.

See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected static double`	`DEFAULT_Z_MAX` A threshold for responses (Friedman suggests between 2 and 4)
`protected Attribute`	`m_ClassAttribute` The actual class attribute (for getting class names)
`protected java.util.ArrayList<Classifier[]>`	`m_Classifiers` ArrayList for storing the generated base classifiers.
`protected Instances`	`m_data` The training data.
`protected double[]`	`m_InitialFs` The initial F scores (0 by default)
`protected double`	`m_logLikelihood` The current loglikelihood.
`protected int`	`m_NumClasses` The number of classes
`protected Instances`	`m_NumericClassData` Dummy dataset with a numeric class
`protected int`	`m_NumGenerated` The number of successfully generated base classifiers.
`protected int`	`m_numThreads` The number of threads to use at prediction time in batch prediction.
`protected double`	`m_Offset` The value by which the actual target value for the true class is offset.
`protected int`	`m_poolSize` The size of the thread pool.
`protected double`	`m_Precision` The threshold on the improvement of the likelihood
`protected double[][]`	`m_probs` The probabilities used during the training process.
`protected java.util.Random`	`m_RandomInstance` The random number generator used
`protected double`	`m_Shrinkage` The value of the shrinkage parameter
`protected double`	`m_sumOfWeights` The total weight of the data.
`protected double[][]`	`m_trainFs` The F scores used during the training process.
`protected double[][]`	`m_trainYs` The y values used during the training process.
`protected boolean`	`m_UseEstimatedPriors` Whether to start with class priors estimated from the training data
`protected boolean`	`m_UseResampling` Use boosting with reweighting?
`protected int`	`m_WeightThreshold` Weight thresholding.
`protected Classifier`	`m_ZeroR` A ZeroR model in case no model can be built from the data
`protected double`	`m_zMax` The Z max value to use

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
m_Seed

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
m_NumIterations

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

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

Constructor Summary

Constructors
Constructor and Description

LogitBoost()
Constructor.

Constructors
Constructor and Description
`LogitBoost()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Method used to build the classifier.
`Classifier[][]`	`classifiers()` Returns the array of classifiers that have been built.
`protected java.lang.String`	`defaultClassifierString()` String describing default classifier.
`double[]`	`distributionForInstance(Instance inst)` Calculates the class membership probabilities for the given test instance.
`double[][]`	`distributionsForInstances(Instances insts)` Calculates the class membership probabilities for the given test instances.
`void`	`done()` Clean up after boosting.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getLikelihoodThreshold()` Get the value of Precision.
`int`	`getNumThreads()` Gets the number of threads.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`int`	`getPoolSize()` Gets the number of threads.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getShrinkage()` Get the value of Shrinkage.
`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.
`boolean`	`getUseEstimatedPriors()` Get whether resampling is turned on
`boolean`	`getUseResampling()` Get whether resampling is turned on
`int`	`getWeightThreshold()` Get the degree of weight thresholding
`double`	`getZMax()` Get the Z max threshold on the responses
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`boolean`	`implementsMoreEfficientBatchPrediction()` Performs efficient batch prediction
`void`	`initializeClassifier(Instances data)` Builds the boosted classifier
`java.lang.String`	`likelihoodThresholdTipText()` 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[] argv)` Main method for testing this class.
`boolean`	`next()` Perform another iteration of boosting.
`java.lang.String`	`numThreadsTipText()`
`java.lang.String`	`poolSizeTipText()`
`protected double[]`	`processInstance(Instance instance)` Applies models to an instance to get class probabilities.
`protected Instances`	`selectWeightQuantile(Instances data, double quantile)` Select only instances with weights that contribute to the specified quantile of the weight distribution
`void`	`setLikelihoodThreshold(double newPrecision)` Set the value of Precision.
`void`	`setNumThreads(int nT)` Sets the number of threads
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setPoolSize(int nT)` Sets the number of threads
`void`	`setShrinkage(double newShrinkage)` Set the value of Shrinkage.
`void`	`setUseEstimatedPriors(boolean r)` Set resampling mode
`void`	`setUseResampling(boolean r)` Set resampling mode
`void`	`setWeightThreshold(int threshold)` Set weight thresholding
`void`	`setZMax(double zMax)` Set the Z max threshold on the responses
`java.lang.String`	`shrinkageTipText()` Returns the tip text for this property
`java.lang.String`	`toSource(java.lang.String className)` Returns the boosted model as Java source code.
`java.lang.String`	`toString()` Returns description of the boosted classifier.
`java.lang.String`	`useEstimatedPriorsTipText()` Returns the tip text for this property
`java.lang.String`	`useResamplingTipText()` Returns the tip text for this property
`java.lang.String`	`weightThresholdTipText()` Returns the tip text for this property
`java.lang.String`	`ZMaxTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
defaultNumberOfIterations, getM_Classifiers, getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

Methods inherited from interface weka.core.BatchPredictor
getBatchSize, setBatchSize

- Field Detail
  - m_Classifiers
```
protected java.util.ArrayList<Classifier[]> m_Classifiers
```
    ArrayList for storing the generated base classifiers. Note: we are hiding the variable from IteratedSingleClassifierEnhancer
  - m_NumClasses
```
protected int m_NumClasses
```
    The number of classes
  - m_NumGenerated
```
protected int m_NumGenerated
```
    The number of successfully generated base classifiers.
  - m_WeightThreshold
```
protected int m_WeightThreshold
```
    Weight thresholding. The percentage of weight mass used in training
  - DEFAULT_Z_MAX
```
protected static final double DEFAULT_Z_MAX
```
    A threshold for responses (Friedman suggests between 2 and 4)
    
    See Also:
    
    Constant Field Values
  - m_NumericClassData
```
protected Instances m_NumericClassData
```
    Dummy dataset with a numeric class
  - m_ClassAttribute
```
protected Attribute m_ClassAttribute
```
    The actual class attribute (for getting class names)
  - m_UseResampling
```
protected boolean m_UseResampling
```
    Use boosting with reweighting?
  - m_Precision
```
protected double m_Precision
```
    The threshold on the improvement of the likelihood
  - m_Shrinkage
```
protected double m_Shrinkage
```
    The value of the shrinkage parameter
  - m_UseEstimatedPriors
```
protected boolean m_UseEstimatedPriors
```
    Whether to start with class priors estimated from the training data
  - m_RandomInstance
```
protected java.util.Random m_RandomInstance
```
    The random number generator used
  - m_Offset
```
protected double m_Offset
```
    The value by which the actual target value for the true class is offset.
  - m_ZeroR
```
protected Classifier m_ZeroR
```
    A ZeroR model in case no model can be built from the data
  - m_InitialFs
```
protected double[] m_InitialFs
```
    The initial F scores (0 by default)
  - m_zMax
```
protected double m_zMax
```
    The Z max value to use
  - m_trainYs
```
protected double[][] m_trainYs
```
    The y values used during the training process.
  - m_trainFs
```
protected double[][] m_trainFs
```
    The F scores used during the training process.
  - m_probs
```
protected double[][] m_probs
```
    The probabilities used during the training process.
  - m_logLikelihood
```
protected double m_logLikelihood
```
    The current loglikelihood.
  - m_sumOfWeights
```
protected double m_sumOfWeights
```
    The total weight of the data.
  - m_data
```
protected Instances m_data
```
    The training data.
  - m_numThreads
```
protected int m_numThreads
```
    The number of threads to use at prediction time in batch prediction.
  - m_poolSize
```
protected int m_poolSize
```
    The size of the thread pool.
- Constructor Detail
  - LogitBoost
```
public LogitBoost()
```
    Constructor.
- 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
  - defaultClassifierString
```
protected java.lang.String defaultClassifierString()
```
    String describing default classifier.
    
    Overrides:
    
    defaultClassifierString in class SingleClassifierEnhancer
    
    Returns:
    
    the default classifier classname
  - selectWeightQuantile
```
protected Instances selectWeightQuantile(Instances data,
                                         double quantile)
```
    Select only instances with weights that contribute to the specified quantile of the weight distribution
    
    Parameters:
    
    data - the input instances
    
    quantile - the specified quantile eg 0.9 to select 90% of the weight mass
    
    Returns:
    
    the selected instances
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableIteratedSingleClassifierEnhancer
    
    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:
```
 -Q
  Use resampling instead of reweighting for boosting.
 
```
```
 -use-estimated-priors
  Use estimated priors rather than uniform ones.
 
```
```
 -P <percent>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 
```
```
 -L <num>
  Threshold on the improvement of the likelihood.
  (default -Double.MAX_VALUE)
 
```
```
 -H <num>
  Shrinkage parameter.
  (default 1)
 
```
```
 -Z <num>
  Z max threshold for responses.
  (default 3)
 
```
```
 -O <int>
  The size of the thread pool, for example, the number of cores in the CPU. (default 1)
 
```
```
 -E <int>
  The number of threads to use for batch prediction, which should be >= size of thread pool.
  (default 1)
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -I <num>
  Number of iterations.
  (default 10)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
```
 Options specific to classifier weka.classifiers.trees.DecisionStump:
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
    Options after -- are passed to the designated learner.
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableIteratedSingleClassifierEnhancer
    
    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 RandomizableIteratedSingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - ZMaxTipText
```
public java.lang.String ZMaxTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setZMax
```
public void setZMax(double zMax)
```
    Set the Z max threshold on the responses
    
    Parameters:
    
    zMax - the threshold to use
  - getZMax
```
public double getZMax()
```
    Get the Z max threshold on the responses
    
    Returns:
    
    the threshold to use
  - shrinkageTipText
```
public java.lang.String shrinkageTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getShrinkage
```
public double getShrinkage()
```
    Get the value of Shrinkage.
    
    Returns:
    
    Value of Shrinkage.
  - setShrinkage
```
public void setShrinkage(double newShrinkage)
```
    Set the value of Shrinkage.
    
    Parameters:
    
    newShrinkage - Value to assign to Shrinkage.
  - likelihoodThresholdTipText
```
public java.lang.String likelihoodThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLikelihoodThreshold
```
public double getLikelihoodThreshold()
```
    Get the value of Precision.
    
    Returns:
    
    Value of Precision.
  - setLikelihoodThreshold
```
public void setLikelihoodThreshold(double newPrecision)
```
    Set the value of Precision.
    
    Parameters:
    
    newPrecision - Value to assign to Precision.
  - useResamplingTipText
```
public java.lang.String useResamplingTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseResampling
```
public void setUseResampling(boolean r)
```
    Set resampling mode
    
    Parameters:
    
    r - true if resampling should be done
  - getUseResampling
```
public boolean getUseResampling()
```
    Get whether resampling is turned on
    
    Returns:
    
    true if resampling output is on
  - useEstimatedPriorsTipText
```
public java.lang.String useEstimatedPriorsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseEstimatedPriors
```
public void setUseEstimatedPriors(boolean r)
```
    Set resampling mode
    
    Parameters:
    
    r - true if resampling should be done
  - getUseEstimatedPriors
```
public boolean getUseEstimatedPriors()
```
    Get whether resampling is turned on
    
    Returns:
    
    true if resampling output is on
  - weightThresholdTipText
```
public java.lang.String weightThresholdTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setWeightThreshold
```
public void setWeightThreshold(int threshold)
```
    Set weight thresholding
    
    Parameters:
    
    threshold - the percentage of weight mass used for training
  - getWeightThreshold
```
public int getWeightThreshold()
```
    Get the degree of weight thresholding
    
    Returns:
    
    the percentage of weight mass used for training
  - numThreadsTipText
```
public java.lang.String numThreadsTipText()
```
    Returns:
    
    a string to describe the option
  - getNumThreads
```
public int getNumThreads()
```
    Gets the number of threads.
  - setNumThreads
```
public void setNumThreads(int nT)
```
    Sets the number of threads
  - poolSizeTipText
```
public java.lang.String poolSizeTipText()
```
    Returns:
    
    a string to describe the option
  - getPoolSize
```
public int getPoolSize()
```
    Gets the number of threads.
  - setPoolSize
```
public void setPoolSize(int nT)
```
    Sets the number of threads
  - 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 SingleClassifierEnhancer
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Method used to build the classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Overrides:
    
    buildClassifier in class IteratedSingleClassifierEnhancer
    
    Parameters:
    
    data - the training data to be used for generating the bagged classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - initializeClassifier
```
public void initializeClassifier(Instances data)
                          throws java.lang.Exception
```
    Builds the boosted classifier
    
    Specified by:
    
    initializeClassifier in interface IterativeClassifier
    
    Parameters:
    
    data - the data to train the classifier with
    
    Throws:
    
    java.lang.Exception - if building fails, e.g., can't handle data
  - next
```
public boolean next()
             throws java.lang.Exception
```
    Perform another iteration of boosting.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Returns:
    
    false if no further iterations could be performed, true otherwise
    
    Throws:
    
    java.lang.Exception - if this iteration fails for unexpected reasons
  - done
```
public void done()
```
    Clean up after boosting.
    
    Specified by:
    
    done in interface IterativeClassifier
  - classifiers
```
public Classifier[][] classifiers()
```
    Returns the array of classifiers that have been built.
    
    Returns:
    
    the built classifiers
  - implementsMoreEfficientBatchPrediction
```
public boolean implementsMoreEfficientBatchPrediction()
```
    Performs efficient batch prediction
    
    Specified by:
    
    implementsMoreEfficientBatchPrediction in interface BatchPredictor
    
    Overrides:
    
    implementsMoreEfficientBatchPrediction in class AbstractClassifier
    
    Returns:
    
    true, as LogitBoost can perform efficient batch prediction
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - processInstance
```
protected double[] processInstance(Instance instance)
                            throws java.lang.Exception
```
    Applies models to an instance to get class probabilities.
    
    Throws:
    
    java.lang.Exception
  - distributionsForInstances
```
public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instances. Uses multi-threading if requested.
    
    Specified by:
    
    distributionsForInstances in interface BatchPredictor
    
    Overrides:
    
    distributionsForInstances in class AbstractClassifier
    
    Parameters:
    
    insts - the instances to be classified
    
    Returns:
    
    predicted class probability distributions
    
    Throws:
    
    java.lang.Exception - if instances could not be classified successfully
  - toSource
```
public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception
```
    Returns the boosted model as Java source code.
    
    Specified by:
    
    toSource in interface Sourcable
    
    Parameters:
    
    className - the classname in the generated code
    
    Returns:
    
    the tree as Java source code
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - toString
```
public java.lang.String toString()
```
    Returns description of the boosted classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    description of the boosted classifier as a string
  - 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[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class LogitBoost

Field Summary

Fields inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Methods inherited from interface weka.core.BatchPredictor

Field Detail

m_Classifiers

m_NumClasses

m_NumGenerated

m_WeightThreshold

DEFAULT_Z_MAX

m_NumericClassData

m_ClassAttribute

m_UseResampling

m_Precision

m_Shrinkage

m_UseEstimatedPriors

m_RandomInstance

m_Offset

m_ZeroR

m_InitialFs

m_zMax

m_trainYs

m_trainFs

m_probs

m_logLikelihood

m_sumOfWeights

m_data

m_numThreads

m_poolSize

Constructor Detail

LogitBoost

Method Detail

globalInfo

getTechnicalInformation

defaultClassifierString

selectWeightQuantile

listOptions

setOptions

getOptions

ZMaxTipText

setZMax

getZMax

shrinkageTipText

getShrinkage

setShrinkage

likelihoodThresholdTipText

getLikelihoodThreshold

setLikelihoodThreshold

useResamplingTipText

setUseResampling

getUseResampling

useEstimatedPriorsTipText

setUseEstimatedPriors

getUseEstimatedPriors

weightThresholdTipText

setWeightThreshold

getWeightThreshold

numThreadsTipText

getNumThreads

setNumThreads

poolSizeTipText

getPoolSize

setPoolSize

getCapabilities

buildClassifier

initializeClassifier

next

done

classifiers