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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.trees.lmt.LogisticBase
public class LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm. Used for building logistic model trees (weka.classifiers.trees.lmt.LMT) and standalone logistic regression (weka.classifiers.functions.SimpleLogistic). Valid options are:
-D If set, classifier is run in debug mode and may output additional info to the console
| Constructor Summary | |
|---|---|
LogisticBase()
Constructor that creates LogisticBase object with standard options. |
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LogisticBase(int numBoostingIterations,
boolean useCrossValidation,
boolean errorOnProbabilities)
Constructor to create LogisticBase object. |
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| Method Summary | |
|---|---|
void |
buildClassifier(Instances data)
Builds the logistic regression model usiing LogitBoost. |
void |
cleanup()
Cleanup in order to save memory. |
double[] |
distributionForInstance(Instance instance)
Returns class probabilities for an instance. |
int |
getMaxIterations()
Returns the maxIterations parameter. |
int |
getNumRegressions()
The number of LogitBoost iterations performed (= the number of simple regression functions fit). |
java.lang.String |
getRevision()
Returns the revision string. |
boolean |
getUseAIC()
Get the value of useAIC. |
int[][] |
getUsedAttributes()
Returns an array of the indices of the attributes used in the logistic model. |
double |
getWeightTrimBeta()
Get the value of weightTrimBeta. |
double |
percentAttributesUsed()
Returns the fraction of all attributes in the data that are used in the logistic model (in percent). |
void |
setHeuristicStop(int heuristicStop)
Sets the option "heuristicStop". |
void |
setMaxIterations(int maxIterations)
Sets the parameter "maxIterations". |
void |
setUseAIC(boolean c)
Set the value of useAIC. |
void |
setWeightTrimBeta(double w)
Sets the option "weightTrimBeta". |
java.lang.String |
toString()
Returns a description of the logistic model (i.e., attributes and coefficients). |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public LogisticBase()
public LogisticBase(int numBoostingIterations,
boolean useCrossValidation,
boolean errorOnProbabilities)
numBoostingIterations - fixed number of iterations for LogitBoost (if negative, use cross-validation or
stopping criterion on the training data).useCrossValidation - cross-validate number of LogitBoost iterations (if false, use stopping
criterion on the training data).errorOnProbabilities - if true, use error on probabilities
instead of misclassification for stopping criterion of LogitBoost| Method Detail |
|---|
public void buildClassifier(Instances data)
throws java.lang.Exception
buildClassifier in class Classifierdata - the training data
java.lang.Exception - if something goes wrongpublic int[][] getUsedAttributes()
public int getNumRegressions()
public double getWeightTrimBeta()
public boolean getUseAIC()
public void setMaxIterations(int maxIterations)
maxIterations - the maximum iterationspublic void setHeuristicStop(int heuristicStop)
heuristicStop - the heuristic stop to usepublic void setWeightTrimBeta(double w)
public void setUseAIC(boolean c)
c - Value to assign to useAIC.public int getMaxIterations()
public double percentAttributesUsed()
public java.lang.String toString()
toString in class java.lang.Object
public double[] distributionForInstance(Instance instance)
throws java.lang.Exception
distributionForInstance in class Classifierinstance - the instance to compute the distribution for
java.lang.Exception - if distribution can't be computed successfullypublic void cleanup()
public java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifier
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