类 FTNode
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.trees.lmt.LogisticBase
weka.classifiers.trees.ft.FTtree
weka.classifiers.trees.ft.FTNode
- 所有已实现的接口:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,WeightedInstancesHandler
Class for Functional tree structure.
- 版本:
- $Revision: 1.4 $
- 作者:
- Jo\~{a}o Gama, Carlos Ferreira
- 另请参阅:
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构造器概要
构造器构造器说明FTNode
(boolean errorOnProbabilities, int numBoostingIterations, int minNumInstances, double weightTrimBeta, boolean useAIC) Constructor for Functional tree node. -
方法概要
修饰符和类型方法说明void
buildClassifier
(Instances data) Method for building a Functional 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.Returns the revision string.double
prune()
Method for prunning a tree using C4.5 pruning procedure.从类继承的方法 weka.classifiers.trees.ft.FTtree
assignIDs, assignLeafModelNumbers, cleanup, getConstError, getModelParameters, getNodes, getNodes, getNumInnerNodes, getNumLeaves, graph, hasModels, modelDistributionForInstance, modelsToString, numLeaves, numNodes, toString
从类继承的方法 weka.classifiers.trees.lmt.LogisticBase
getMaxIterations, getNumRegressions, getUseAIC, getUsedAttributes, getWeightTrimBeta, percentAttributesUsed, setHeuristicStop, setMaxIterations, setUseAIC, setWeightTrimBeta
从类继承的方法 weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions
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构造器详细资料
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FTNode
public FTNode(boolean errorOnProbabilities, int numBoostingIterations, int minNumInstances, double weightTrimBeta, boolean useAIC) Constructor for Functional tree node.- 参数:
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 splitting
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方法详细资料
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buildClassifier
Method for building a Functional tree (only called for the root node). Grows an initial Functional Tree.- 指定者:
buildClassifier
在类中FTtree
- 参数:
data
- the data to train with- 抛出:
Exception
- if something goes wrong
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buildTree
public void buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters) throws Exception Method for building the tree structure. Builds a logistic model, splits the node and recursively builds tree for child nodes.- 指定者:
buildTree
在类中FTtree
- 参数:
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- 抛出:
Exception
- if something goes wrong
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prune
Method for prunning a tree using C4.5 pruning procedure. -
distributionForInstance
Returns the class probabilities for an instance given by the Functional Tree.- 指定者:
distributionForInstance
在类中FTtree
- 参数:
instance
- the instance- 返回:
- the array of probabilities
- 抛出:
Exception
- if distribution can't be computed successfully
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getRevision
Returns the revision string.- 指定者:
getRevision
在接口中RevisionHandler
- 覆盖:
getRevision
在类中FTtree
- 返回:
- the revision
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