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.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

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

serialVersionUID: 4154189200352566053L

Serialized Fields

m_train

Instances m_train
Training data.


m_Successors

SimpleCart[] m_Successors
Successor nodes.


m_Attribute

Attribute m_Attribute
Attribute used to split data.


m_SplitValue

double m_SplitValue
Split point for a numeric attribute.


m_SplitString

java.lang.String m_SplitString
Split subset used to split data for nominal attributes.


m_ClassValue

double m_ClassValue
Class value if the node is leaf.


m_ClassAttribute

Attribute m_ClassAttribute
Class attriubte of data.


m_minNumObj

double m_minNumObj
Minimum number of instances in at the terminal nodes.


m_numFoldsPruning

int m_numFoldsPruning
Number of folds for minimal cost-complexity pruning.


m_Alpha

double m_Alpha
Alpha-value (for pruning) at the node.


m_numIncorrectModel

double m_numIncorrectModel
Number of training examples misclassified by the model (subtree rooted).


m_numIncorrectTree

double m_numIncorrectTree
Number of training examples misclassified by the model (subtree not rooted).


m_isLeaf

boolean m_isLeaf
Indicate if the node is a leaf node.


m_Prune

boolean m_Prune
If use minimal cost-compexity pruning.


m_totalTrainInstances

int m_totalTrainInstances
Total number of instances used to build the classifier.


m_Props

double[] m_Props
Proportion for each branch.


m_ClassProbs

double[] m_ClassProbs
Class probabilities.


m_Distribution

double[] m_Distribution
Distributions of leaf node (or temporary leaf node in minimal cost-complexity pruning)


m_Heuristic

boolean m_Heuristic
If use huristic search for nominal attributes in multi-class problems (default true).


m_UseOneSE

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


m_SizePer

double m_SizePer
Training data size.