Index

A B C D E F G H L M N P S T U W

A

assignIDs(int) - Method in class weka.classifiers.trees.ft.FTtree: Assigns unique IDs to all nodes in the tree
assignLeafModelNumbers(int) - Method in class weka.classifiers.trees.ft.FTtree: Assigns numbers to the logistic regression models at the leaves of the tree

B

binSplitTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
buildClassifier(Instances) - Method in class weka.classifiers.trees.FT: Builds the classifier.
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTInnerNode: Method for building a Functional Inner tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Method for building a Functional Leaves tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTNode: Method for building a Functional tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.FTtree: Method for building a Functional Tree (only called for the root node).
buildClassifier(Instances) - Method in class weka.classifiers.trees.ft.LogisticBase: Builds the logistic regression model usiing LogitBoost.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTInnerNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTNode: Method for building the tree structure.
buildTree(Instances, SimpleLinearRegression[][], double, double) - Method in class weka.classifiers.trees.ft.FTtree: Abstract method for building the tree structure.

C

classifyInstance(Instance) - Method in class weka.classifiers.trees.FT: Classifies an instance.
cleanup() - Method in class weka.classifiers.trees.ft.FTtree: Cleanup in order to save memory.
cleanup() - Method in class weka.classifiers.trees.ft.LogisticBase: Cleanup in order to save memory.

D

distributionForInstance(Instance) - Method in class weka.classifiers.trees.FT: Returns class probabilities for an instance.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTInnerNode: Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTLeavesNode: Returns the class probabilities for an instance given by the Functional Leaves tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTNode: Returns the class probabilities for an instance given by the Functional Tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree: Returns the class probabilities for an instance given by the Functional tree.
distributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.LogisticBase: Returns class probabilities for an instance.

E

enumerateMeasures() - Method in class weka.classifiers.trees.FT: Returns an enumeration of the additional measure names
errorOnProbabilitiesTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property

F

FT - Class in weka.classifiers.trees: Builds 'functional trees' for classification, more specifically, functional trees with logistic regression functions at the inner nodes and/or leaves.
FT() - Constructor for class weka.classifiers.trees.FT: Creates an instance of FT with standard options
FTInnerNode - Class in weka.classifiers.trees.ft: Class for Functional Inner tree structure.
FTInnerNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTInnerNode: Constructor for Functional Inner tree node.
FTLeavesNode - Class in weka.classifiers.trees.ft: Class for Functional Leaves tree version.
FTLeavesNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTLeavesNode: Constructor for Functional Leaves tree node.
FTNode - Class in weka.classifiers.trees.ft: Class for Functional tree structure.
FTNode(boolean, int, int, double, boolean) - Constructor for class weka.classifiers.trees.ft.FTNode: Constructor for Functional tree node.
FTtree - Class in weka.classifiers.trees.ft: Abstract class for Functional tree structure.
FTtree() - Constructor for class weka.classifiers.trees.ft.FTtree

G

getBinSplit() - Method in class weka.classifiers.trees.FT: Get the value of binarySplits.
getCapabilities() - Method in class weka.classifiers.trees.FT: Returns default capabilities of the classifier.
getConstError(double[]) - Method in class weka.classifiers.trees.ft.FTtree
getErrorOnProbabilities() - Method in class weka.classifiers.trees.FT: Get the value of errorOnProbabilities.
getMaxIterations() - Method in class weka.classifiers.trees.ft.LogisticBase: Returns the maxIterations parameter.
getMeasure(String) - Method in class weka.classifiers.trees.FT: Returns the value of the named measure
getMinNumInstances() - Method in class weka.classifiers.trees.FT: Get the value of minNumInstances.
getModelParameters() - Method in class weka.classifiers.trees.ft.FTtree: Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
getModelType() - Method in class weka.classifiers.trees.FT: Get the type of functional tree model being used.
getNodes() - Method in class weka.classifiers.trees.ft.FTtree: Return a list of all inner nodes in the tree
getNodes(Vector) - Method in class weka.classifiers.trees.ft.FTtree: Fills a list with all inner nodes in the tree
getNumBoostingIterations() - Method in class weka.classifiers.trees.FT: Get the value of numBoostingIterations.
getNumInnerNodes() - Method in class weka.classifiers.trees.ft.FTtree: Method to count the number of inner nodes in the tree
getNumLeaves() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of leaves in the tree.
getNumRegressions() - Method in class weka.classifiers.trees.ft.LogisticBase: The number of LogitBoost iterations performed (= the number of simple regression functions fit).
getOptions() - Method in class weka.classifiers.trees.FT: Gets the current settings of the Classifier.
getRevision() - Method in class weka.classifiers.trees.ft.FTInnerNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTLeavesNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTNode: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.FTtree: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.FT: Returns the revision string.
getRevision() - Method in class weka.classifiers.trees.ft.LogisticBase: Returns the revision string.
getTechnicalInformation() - Method in class weka.classifiers.trees.FT: 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.
getUseAIC() - Method in class weka.classifiers.trees.FT: Get the value of useAIC.
getUseAIC() - Method in class weka.classifiers.trees.ft.LogisticBase: Get the value of useAIC.
getUsedAttributes() - Method in class weka.classifiers.trees.ft.LogisticBase: Returns an array of the indices of the attributes used in the logistic model.
getWeightTrimBeta() - Method in class weka.classifiers.trees.FT: Get the value of weightTrimBeta.
getWeightTrimBeta() - Method in class weka.classifiers.trees.ft.LogisticBase: Get the value of weightTrimBeta.
globalInfo() - Method in class weka.classifiers.trees.FT: Returns a string describing classifier
graph() - Method in class weka.classifiers.trees.ft.FTtree: Returns graph describing the tree.
graph() - Method in class weka.classifiers.trees.FT: Returns graph describing the tree.
graphType() - Method in class weka.classifiers.trees.FT: Returns the type of graph this classifier represents.

H

hasModels() - Method in class weka.classifiers.trees.ft.FTtree: Returns true if the logistic regression model at this node has changed compared to the one at the parent node.

L

listOptions() - Method in class weka.classifiers.trees.FT: Returns an enumeration describing the available options.
LogisticBase - Class in weka.classifiers.trees.ft: Base/helper class for building logistic regression models with the LogitBoost algorithm.
LogisticBase() - Constructor for class weka.classifiers.trees.ft.LogisticBase: Constructor that creates LogisticBase object with standard options.
LogisticBase(int, boolean, boolean) - Constructor for class weka.classifiers.trees.ft.LogisticBase: Constructor to create LogisticBase object.

M

main(String[]) - Static method in class weka.classifiers.trees.FT: Main method for testing this class
measureNumLeaves() - Method in class weka.classifiers.trees.FT: Returns the number of leaves in the tree
measureTreeSize() - Method in class weka.classifiers.trees.FT: Returns the size of the tree
minNumInstancesTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
MODEL_FT - Static variable in class weka.classifiers.trees.FT: model types
MODEL_FTInner - Static variable in class weka.classifiers.trees.FT
MODEL_FTLeaves - Static variable in class weka.classifiers.trees.FT
modelDistributionForInstance(Instance) - Method in class weka.classifiers.trees.ft.FTtree: Returns the class probabilities for an instance according to the logistic model at the node.
modelsToString() - Method in class weka.classifiers.trees.ft.FTtree: Returns a string describing the logistic regression function at the node.
modelTypeTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property

N

numBoostingIterationsTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
numLeaves() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of leaves (normal count).
numNodes() - Method in class weka.classifiers.trees.ft.FTtree: Returns the number of nodes.

P

percentAttributesUsed() - Method in class weka.classifiers.trees.ft.LogisticBase: Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
prune() - Method in class weka.classifiers.trees.ft.FTInnerNode: Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTLeavesNode: Prunes a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTNode: Method for prunning a tree using C4.5 pruning procedure.
prune() - Method in class weka.classifiers.trees.ft.FTtree: Abstract Method that prunes a tree using C4.5 pruning procedure.

S

setBinSplit(boolean) - Method in class weka.classifiers.trees.FT: Set the value of binarySplits.
setErrorOnProbabilities(boolean) - Method in class weka.classifiers.trees.FT: Set the value of errorOnProbabilities.
setHeuristicStop(int) - Method in class weka.classifiers.trees.ft.LogisticBase: Sets the option "heuristicStop".
setMaxIterations(int) - Method in class weka.classifiers.trees.ft.LogisticBase: Sets the parameter "maxIterations".
setMinNumInstances(int) - Method in class weka.classifiers.trees.FT: Set the value of minNumInstances.
setModelType(SelectedTag) - Method in class weka.classifiers.trees.FT: Set the Functional Tree type.
setNumBoostingIterations(int) - Method in class weka.classifiers.trees.FT: Set the value of numBoostingIterations.
setOptions(String[]) - Method in class weka.classifiers.trees.FT: Parses a given list of options.
setUseAIC(boolean) - Method in class weka.classifiers.trees.ft.LogisticBase: Set the value of useAIC.
setUseAIC(boolean) - Method in class weka.classifiers.trees.FT: Set the value of useAIC.
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.ft.LogisticBase: Sets the option "weightTrimBeta".
setWeightTrimBeta(double) - Method in class weka.classifiers.trees.FT: Set the value of weightTrimBeta.

T

TAGS_MODEL - Static variable in class weka.classifiers.trees.FT: possible model types.
toString() - Method in class weka.classifiers.trees.ft.FTtree: Returns a description of the Functional tree (tree structure and logistic models)
toString() - Method in class weka.classifiers.trees.ft.LogisticBase: Returns a description of the logistic model (i.e., attributes and coefficients).
toString() - Method in class weka.classifiers.trees.FT: Returns a description of the classifier.

U

useAICTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property

W

weightTrimBetaTipText() - Method in class weka.classifiers.trees.FT: Returns the tip text for this property
weka.classifiers.trees - package weka.classifiers.trees
weka.classifiers.trees.ft - package weka.classifiers.trees.ft

A B C D E F G H L M N P S T U W