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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.trees.lmt.LogisticBase
weka.classifiers.trees.ft.FTtree
weka.classifiers.trees.ft.FTInnerNode
public class FTInnerNode
Class for Functional Inner tree structure.
Constructor Summary | |
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FTInnerNode(boolean errorOnProbabilities,
int numBoostingIterations,
int minNumInstances,
double weightTrimBeta,
boolean useAIC)
Constructor for Functional Inner tree node. |
Method Summary | |
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void |
buildClassifier(Instances data)
Method for building a Functional Inner tree (only called for the root node). |
void |
buildTree(Instances data,
SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight,
double higherNumParameters)
Method for building the tree structure. |
double[] |
distributionForInstance(Instance instance)
Returns the class probabilities for an instance given by the Functional tree. |
java.lang.String |
getRevision()
Returns the revision string. |
double |
prune()
Prunes a tree using C4.5 pruning procedure. |
Methods inherited from class weka.classifiers.trees.ft.FTtree |
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assignIDs, assignLeafModelNumbers, cleanup, getConstError, getModelParameters, getNodes, getNodes, getNumInnerNodes, getNumLeaves, graph, hasModels, modelDistributionForInstance, modelsToString, numLeaves, numNodes, toString |
Methods inherited from class weka.classifiers.trees.lmt.LogisticBase |
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getMaxIterations, getNumRegressions, getUseAIC, getUsedAttributes, getWeightTrimBeta, percentAttributesUsed, setHeuristicStop, setMaxIterations, setUseAIC, setWeightTrimBeta |
Methods inherited from class weka.classifiers.Classifier |
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classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions |
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 FTInnerNode(boolean errorOnProbabilities, int numBoostingIterations, int minNumInstances, double weightTrimBeta, boolean useAIC)
errorOnProbabilities
- Use error on probabilities for stopping criterion of LogitBoost?numBoostingIterations
- sets the numBoostingIterations parameterminNumInstances
- minimum number of instances at which a node is considered for splittingMethod Detail |
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public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in class FTtree
data
- the data to train with
java.lang.Exception
- if something goes wrongpublic void buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) throws java.lang.Exception
buildTree
in class FTtree
data
- the training data passed on to this nodehigherRegressions
- An array of regression functions produced by LogitBoost at higher
levels in the tree. They represent a logistic regression model that is refined locally
at this node.totalInstanceWeight
- the total number of training exampleshigherNumParameters
- effective number of parameters in the logistic regression model built
in parent nodes
java.lang.Exception
- if something goes wrongpublic double prune() throws java.lang.Exception
prune
in class FTtree
java.lang.Exception
- if something goes wrongpublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance
in class FTtree
instance
- the instance
java.lang.Exception
- if distribution can't be computed successfullypublic java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class FTtree
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