Skip navigation links
B C D G I K L M N S T W 

B

buildClassifier(Instances) - Method in class weka.classifiers.trees.CDT
Builds classifier.

C

CDT - Class in weka.classifiers.trees
Decision tree learner based on imprecise probabilities and uncertainty measures.
CDT() - Constructor for class weka.classifiers.trees.CDT
 

D

distributionForInstance(Instance) - Method in class weka.classifiers.trees.CDT
Computes class distribution of an instance using the tree.

G

getKTHRootAttribute() - Method in class weka.classifiers.trees.CDT
This method gets the position of the node in a rank to be used as root node in a decision tree (for the IRNV ensemble).
getOptions() - Method in class weka.classifiers.trees.CDT
Gets options from this classifier.
getRevision() - Method in class weka.classifiers.trees.CDT
Returns the revision string.
getSValue() - Method in class weka.classifiers.trees.CDT
This method gets the s parameter used in the Imprecise Dirichlet model to obtain imprecise probabilities.
globalInfo() - Method in class weka.classifiers.trees.CDT
Returns a string describing classifier
graph() - Method in class weka.classifiers.trees.CDT
Outputs the decision tree as a graph.

I

impreciseEntropy(double[], double) - Static method in class weka.core.ImpreciseEntropyContingencyTables
Computes the entropy based on imprecise probabilities of the given array.
impreciseEntropyConditionedOnRows(double[][], double) - Static method in class weka.core.ImpreciseEntropyContingencyTables
Computes conditional entropy (based on imprecise probabilities) of the columns given the rows.
ImpreciseEntropyContingencyTables - Class in weka.core
This class inherits from the weka.core.ContingencyTables class of weka; The added functionality is for implementing a new entropy measure based on imprecise probabilities and uncertainty measures.
ImpreciseEntropyContingencyTables() - Constructor for class weka.core.ImpreciseEntropyContingencyTables
 
impreciseEntropyOverColumns(double[][], double) - Static method in class weka.core.ImpreciseEntropyContingencyTables
Computes the columns' entropy (based on imprecise probabilities) for the given contingency table.

K

KTHRootAttributeTipText() - Method in class weka.classifiers.trees.CDT
Returns the tip text for this property

L

listOptions() - Method in class weka.classifiers.trees.CDT
Lists the command-line options for this classifier.

M

main(String[]) - Static method in class weka.classifiers.trees.CDT
Main method for this class.
main(String[]) - Static method in class weka.core.ImpreciseEntropyContingencyTables
Main method for testing this class.

N

numNodes() - Method in class weka.classifiers.trees.CDT
Computes size of the tree.

S

sDistribution(double[], int, double) - Static method in class weka.core.ImpreciseEntropyContingencyTables
This method distributes the mass of "s" to calculate the maximum entropy.
setKTHRootAttribute(int) - Method in class weka.classifiers.trees.CDT
This method stores the position of the node in a rank to be used as root node in a decision tree (for the IRNV ensemble).
setOptions(String[]) - Method in class weka.classifiers.trees.CDT
Parses a given list of options.
setSValue(double) - Method in class weka.classifiers.trees.CDT
Set the s parameter used in the Imprecise Dirichlet model to obtain imprecise probabilities.
SValueTipText() - Method in class weka.classifiers.trees.CDT
Returns the tip text for this property

T

toSource(String) - Method in class weka.classifiers.trees.CDT
Returns the tree as if-then statements.
toString() - Method in class weka.classifiers.trees.CDT
Outputs the decision tree.

W

weka.classifiers.trees - package weka.classifiers.trees
 
weka.core - package weka.core
 
B C D G I K L M N S T W 
Skip navigation links