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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
Friedman, J., T. Hastie and R. Tibshirani (1998) Additive Logistic Regression: a Statistical View of Boosting download postscript.
Valid options are:
-D
Turn on debugging output.
-W classname
Specify the full class name of a weak learner as the basis for
boosting (required).
-I num
Set the number of boost iterations (default 10).
-Q
Use resampling instead of reweighting.
-S seed
Random number seed for resampling (default 1).
-P num
Set the percentage of weight mass used to build classifiers
(default 100).
-F num
Set number of folds for the internal cross-validation
(default 0 -- no cross-validation).
-R num
Set number of runs for the internal cross-validation
(default 1).
-L num
Set the threshold for the improvement of the
average loglikelihood (default -Double.MAX_VALUE).
-H num
Set the value of the shrinkage parameter (default 1).
Options after -- are passed to the designated learner.
Constructor Summary | |
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LogitBoost()
Constructor. |
Method Summary | |
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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. |
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. |
double |
getShrinkage()
Get the value of Shrinkage. |
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 |
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getSeed, seedTipText, setSeed |
Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer |
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getNumIterations, numIterationsTipText, setNumIterations |
Methods inherited from class weka.classifiers.SingleClassifierEnhancer |
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classifierTipText, getClassifier, setClassifier |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public LogitBoost()
Method Detail |
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public java.lang.String globalInfo()
public java.util.Enumeration listOptions()
listOptions
in interface OptionHandler
listOptions
in class RandomizableIteratedSingleClassifierEnhancer
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-D
Turn on debugging output.
-W classname
Specify the full class name of a weak learner as the basis for
boosting (required).
-I num
Set the number of boost iterations (default 10).
-Q
Use resampling instead of reweighting.
-S seed
Random number seed for resampling (default 1).
-P num
Set the percentage of weight mass used to build classifiers
(default 100).
-F num
Set number of folds for the internal cross-validation
(default 0 -- no cross-validation).
-R num
Set number of runs for the internal cross-validation
(default 1.
-L num
Set the threshold for the improvement of the
average loglikelihood (default -Double.MAX_VALUE).
-H num
Set the value of the shrinkage parameter (default 1).
Options after -- are passed to the designated learner.
setOptions
in interface OptionHandler
setOptions
in class RandomizableIteratedSingleClassifierEnhancer
options
- 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 OptionHandler
getOptions
in class RandomizableIteratedSingleClassifierEnhancer
public 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)
resampling
- true if resampling should be donepublic boolean getUseResampling()
public java.lang.String weightThresholdTipText()
public void setWeightThreshold(int threshold)
thresholding
- the percentage of weight mass used for trainingpublic int getWeightThreshold()
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class IteratedSingleClassifierEnhancer
data
- the training data to be used for generating the
bagged classifier.
java.lang.Exception
- if the classifier could not be built successfullypublic Classifier[][] classifiers()
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class Classifier
instance
- the instance to be classified
java.lang.Exception
- if instance could not be classified
successfullypublic java.lang.String toSource(java.lang.String className) throws java.lang.Exception
toSource
in interface Sourcable
className
- the name that should be given to the source class.
java.lang.Exception
- if something goes wrongpublic java.lang.String toString()
toString
in class java.lang.Object
public static void main(java.lang.String[] argv)
argv
- the options
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