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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.IteratedSingleClassifierEnhancer
weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
weka.classifiers.meta.LogitBoost
public class LogitBoost
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.
Can do efficient internal cross-validation to determine appropriate number of iterations.
@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.
-P <percent> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-F <num> Number of folds for internal cross-validation. (default 0 -- no cross-validation)
-R <num> Number of runs for internal cross-validation. (default 1)
-L <num> Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE)
-H <num> Shrinkage parameter. (default 1)
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions after -- are passed to the designated learner.
| Constructor Summary | |
|---|---|
LogitBoost()
Constructor. |
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances data)
Builds the boosted classifier |
Classifier[][] |
classifiers()
Returns the array of classifiers that have been built. |
double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
double |
getLikelihoodThreshold()
Get the value of Precision. |
int |
getNumFolds()
Get the value of NumFolds. |
int |
getNumRuns()
Get the value of NumRuns. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
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 |
getUseResampling()
Get whether resampling is turned on |
int |
getWeightThreshold()
Get the degree of weight thresholding |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.lang.String |
likelihoodThresholdTipText()
Returns the tip text for this property |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
numFoldsTipText()
Returns the tip text for this property |
java.lang.String |
numRunsTipText()
Returns the tip text for this property |
void |
setLikelihoodThreshold(double newPrecision)
Set the value of Precision. |
void |
setNumFolds(int newNumFolds)
Set the value of NumFolds. |
void |
setNumRuns(int newNumRuns)
Set the value of NumRuns. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setShrinkage(double newShrinkage)
Set the value of Shrinkage. |
void |
setUseResampling(boolean r)
Set resampling mode |
void |
setWeightThreshold(int threshold)
Set weight thresholding |
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 |
useResamplingTipText()
Returns the tip text for this property |
java.lang.String |
weightThresholdTipText()
Returns the tip text for this property |
| Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer |
|---|
getSeed, seedTipText, setSeed |
| Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer |
|---|
getNumIterations, numIterationsTipText, setNumIterations |
| Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
|---|
classifierTipText, getClassifier, setClassifier |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public LogitBoost()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-Q Use resampling instead of reweighting for boosting.
-P <percent> Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)
-F <num> Number of folds for internal cross-validation. (default 0 -- no cross-validation)
-R <num> Number of runs for internal cross-validation. (default 1)
-L <num> Threshold on the improvement of the likelihood. (default -Double.MAX_VALUE)
-H <num> Shrinkage parameter. (default 1)
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)
Options specific to classifier weka.classifiers.trees.DecisionStump:
-D If set, classifier is run in debug mode and may output additional info to the consoleOptions after -- are passed to the designated learner.
setOptions in interface OptionHandlersetOptions in class RandomizableIteratedSingleClassifierEnhanceroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class RandomizableIteratedSingleClassifierEnhancerpublic java.lang.String shrinkageTipText()
public double getShrinkage()
public void setShrinkage(double newShrinkage)
newShrinkage - Value to assign to Shrinkage.public java.lang.String likelihoodThresholdTipText()
public double getLikelihoodThreshold()
public void setLikelihoodThreshold(double newPrecision)
newPrecision - Value to assign to Precision.public java.lang.String numRunsTipText()
public int getNumRuns()
public void setNumRuns(int newNumRuns)
newNumRuns - Value to assign to NumRuns.public java.lang.String numFoldsTipText()
public int getNumFolds()
public void setNumFolds(int newNumFolds)
newNumFolds - Value to assign to NumFolds.public java.lang.String useResamplingTipText()
public void setUseResampling(boolean r)
r - true if resampling should be donepublic boolean getUseResampling()
public java.lang.String weightThresholdTipText()
public void setWeightThreshold(int threshold)
threshold - the percentage of weight mass used for trainingpublic int getWeightThreshold()
public Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilities
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class IteratedSingleClassifierEnhancerdata - the data to train the classifier with
java.lang.Exception - if building fails, e.g., can't handle datapublic Classifier[][] classifiers()
public double[] distributionForInstance(Instance instance)
throws java.lang.Exception
distributionForInstance in class Classifierinstance - the instance to be classified
java.lang.Exception - if instance could not be classified
successfully
public java.lang.String toSource(java.lang.String className)
throws java.lang.Exception
toSource in interface SourcableclassName - the classname in the generated code
java.lang.Exception - if something goes wrongpublic java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the options
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