Serialized Form


Package weka.classifiers.trees

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.FT extends AbstractClassifier implements Serializable

serialVersionUID: -1113212459618105000L

Serialized Fields

m_replaceMissing

ReplaceMissingValues m_replaceMissing
Filter to replace missing values


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to replace nominal attributes


m_tree

FTtree m_tree
root of the logistic model tree


m_convertNominal

boolean m_convertNominal
convert nominal attributes to binary ?


m_errorOnProbabilities

boolean m_errorOnProbabilities
use error on probabilties instead of misclassification for stopping criterion of LogitBoost?


m_minNumInstances

int m_minNumInstances
minimum number of instances at which a node is considered for splitting


m_numBoostingIterations

int m_numBoostingIterations
if non-zero, use fixed number of iterations for LogitBoost


m_modelType

int m_modelType
Model Type, value: 0 is FT, 1 is FTLeaves, 2 is FTInner


m_weightTrimBeta

double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as a percentage of total weights) are not included in the regression fit.


m_useAIC

boolean m_useAIC
If true, the AIC is used to choose the best LogitBoost iteration

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

Package weka.classifiers.trees.ft

Class weka.classifiers.trees.ft.FTInnerNode extends FTtree implements Serializable

serialVersionUID: -1125334488640233181L

Class weka.classifiers.trees.ft.FTLeavesNode extends FTtree implements Serializable

serialVersionUID: 950601378326259315L

Class weka.classifiers.trees.ft.FTNode extends FTtree implements Serializable

serialVersionUID: 2317688685139295063L

Class weka.classifiers.trees.ft.FTtree extends LogisticBase implements Serializable

serialVersionUID: 1862737145870398755L

Serialized Fields

m_totalInstanceWeight

double m_totalInstanceWeight
Total number of training instances.


m_id

int m_id
Node id


m_leafModelNum

int m_leafModelNum
ID of logistic model at leaf


m_minNumInstances

int m_minNumInstances
minimum number of instances at which a node is considered for splitting


m_modelSelection

ModelSelection m_modelSelection
ModelSelection object (for splitting)


m_nominalToBinary

NominalToBinary m_nominalToBinary
Filter to convert nominal attributes to binary


m_higherRegressions

SimpleLinearRegression[][] m_higherRegressions
Simple regression functions fit by LogitBoost at higher levels in the tree


m_numHigherRegressions

int m_numHigherRegressions
Number of simple regression functions fit by LogitBoost at higher levels in the tree


m_numInstances

int m_numInstances
Number of instances at the node


m_localModel

ClassifierSplitModel m_localModel
The ClassifierSplitModel (for splitting)


m_auxLocalModel

ClassifierSplitModel m_auxLocalModel
Auxiliary copy ClassifierSplitModel (for splitting)


m_sons

FTtree[] m_sons
Array of children of the node


m_leafclass

int m_leafclass
Stores leaf class value


m_isLeaf

boolean m_isLeaf
True if node is leaf


m_hasConstr

boolean m_hasConstr
True if node has or splits on constructor


m_constError

double m_constError
Constructor error


m_CF

float m_CF
Confidence level