Serialized Form


Package weka.classifiers.trees

Class weka.classifiers.trees.BFTree extends RandomizableClassifier implements Serializable

serialVersionUID: -7035607375962528217L

Serialized Fields

m_PruningStrategy

int m_PruningStrategy
the pruning strategy


m_Successors

BFTree[] m_Successors
Successor nodes.


m_Attribute

Attribute m_Attribute
Attribute used for splitting.


m_SplitValue

double m_SplitValue
Split point (for numeric attributes).


m_SplitString

java.lang.String m_SplitString
Split subset (for nominal attributes).


m_ClassValue

double m_ClassValue
Class value for a node.


m_ClassAttribute

Attribute m_ClassAttribute
Class attribute of a dataset.


m_minNumObj

int m_minNumObj
Minimum number of instances at leaf nodes.


m_numFoldsPruning

int m_numFoldsPruning
Number of folds for the pruning.


m_isLeaf

boolean m_isLeaf
If the ndoe is leaf node.


m_FixedExpansion

int m_FixedExpansion
Fixed number of expansions (if no pruning method is used, its value is -1. Otherwise, its value is gotten from internal cross-validation).


m_Heuristic

boolean m_Heuristic
If use huristic search for binary split (default true). Note even if its value is true, it is only used when the number of values of a nominal attribute is larger than 4.


m_UseGini

boolean m_UseGini
If use Gini index as the splitting criterion - default (if not, information is used).


m_UseErrorRate

boolean m_UseErrorRate
If use error rate in internal cross-validation to fix the number of expansions - default (if not, root mean squared error is used).


m_UseOneSE

boolean m_UseOneSE
If use the 1SE rule to make the decision.


m_Distribution

double[] m_Distribution
Class distributions.


m_Props

double[] m_Props
Branch proportions.


m_SortedIndices

int[][] m_SortedIndices
Sorted indices.


m_Weights

double[][] m_Weights
Sorted weights.


m_Dists

double[][][] m_Dists
Distributions of each attribute for two successor nodes.


m_ClassProbs

double[] m_ClassProbs
Class probabilities.


m_TotalWeight

double m_TotalWeight
Total weights.


m_SizePer

double m_SizePer
The training data size (0-1). Default 1.

Class weka.classifiers.trees.DecisionStump extends AbstractClassifier implements Serializable

serialVersionUID: 1618384535950391L

Serialized Fields

m_AttIndex

int m_AttIndex

m_SplitPoint

double m_SplitPoint

m_Distribution

double[][] m_Distribution

m_Instances

Instances m_Instances

m_ZeroR

Classifier m_ZeroR

Class weka.classifiers.trees.J48 extends AbstractClassifier implements Serializable

serialVersionUID: -217733168393644444L

Serialized Fields

m_root

ClassifierTree m_root

m_unpruned

boolean m_unpruned

m_collapseTree

boolean m_collapseTree

m_CF

float m_CF

m_minNumObj

int m_minNumObj

m_useMDLcorrection

boolean m_useMDLcorrection

m_useLaplace

boolean m_useLaplace

m_reducedErrorPruning

boolean m_reducedErrorPruning

m_numFolds

int m_numFolds

m_binarySplits

boolean m_binarySplits

m_subtreeRaising

boolean m_subtreeRaising

m_noCleanup

boolean m_noCleanup

m_Seed

int m_Seed

Class weka.classifiers.trees.LMT extends AbstractClassifier implements Serializable

serialVersionUID: -1113212459618104943L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing

m_nominalToBinary

NominalToBinary m_nominalToBinary

m_tree

LMTNode m_tree

m_fastRegression

boolean m_fastRegression

m_convertNominal

boolean m_convertNominal

m_splitOnResiduals

boolean m_splitOnResiduals

m_errorOnProbabilities

boolean m_errorOnProbabilities

m_minNumInstances

int m_minNumInstances

m_numBoostingIterations

int m_numBoostingIterations

m_weightTrimBeta

double m_weightTrimBeta

m_useAIC

boolean m_useAIC

Class weka.classifiers.trees.M5P extends M5Base implements Serializable

serialVersionUID: -6118439039768244417L

Class weka.classifiers.trees.RandomForest extends AbstractClassifier implements Serializable

serialVersionUID: 4216839470751428698L

Serialized Fields

m_numTrees

int m_numTrees

m_numFeatures

int m_numFeatures

m_randomSeed

int m_randomSeed

m_KValue

int m_KValue

m_bagger

Bagging m_bagger

m_MaxDepth

int m_MaxDepth

m_numExecutionSlots

int m_numExecutionSlots

Class weka.classifiers.trees.RandomTree extends AbstractClassifier implements Serializable

serialVersionUID: 8934314652175299374L

Serialized Fields

m_Successors

RandomTree[] m_Successors

m_Attribute

int m_Attribute

m_SplitPoint

double m_SplitPoint

m_Info

Instances m_Info

m_Prop

double[] m_Prop

m_ClassDistribution

double[] m_ClassDistribution

m_MinNum

double m_MinNum

m_KValue

int m_KValue

m_randomSeed

int m_randomSeed

m_MaxDepth

int m_MaxDepth

m_NumFolds

int m_NumFolds

m_AllowUnclassifiedInstances

boolean m_AllowUnclassifiedInstances

m_ZeroR

Classifier m_ZeroR

Class weka.classifiers.trees.REPTree extends AbstractClassifier implements Serializable

serialVersionUID: -9216785998198681299L

Serialized Fields

m_zeroR

ZeroR m_zeroR

m_Tree

REPTree.Tree m_Tree

m_NumFolds

int m_NumFolds

m_Seed

int m_Seed

m_NoPruning

boolean m_NoPruning

m_MinNum

double m_MinNum

m_MinVarianceProp

double m_MinVarianceProp

m_MaxDepth

int m_MaxDepth

m_InitialCount

double m_InitialCount

m_SpreadInitialCount

boolean m_SpreadInitialCount

Class weka.classifiers.trees.REPTree.Tree extends java.lang.Object implements Serializable

serialVersionUID: -1635481717888437935L

Serialized Fields

m_Info

Instances m_Info

m_Successors

REPTree.Tree[] m_Successors

m_Attribute

int m_Attribute

m_SplitPoint

double m_SplitPoint

m_Prop

double[] m_Prop

m_ClassProbs

double[] m_ClassProbs

m_Distribution

double[] m_Distribution

m_HoldOutDist

double[] m_HoldOutDist

m_HoldOutError

double m_HoldOutError