Serialized Form

Package weka.classifiers.trees
- Class weka.classifiers.trees.DecisionStump extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  1618384535950391L
  - Serialized Fields
    - m_AttIndex
```
int m_AttIndex
```
    - m_SplitPoint
```
double m_SplitPoint
```
    - m_Distribution
```
double[][] m_Distribution
```
    - m_Instances
```
weka.core.Instances m_Instances
```
    - m_ZeroR
```
weka.classifiers.Classifier m_ZeroR
```
- Class weka.classifiers.trees.FT extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -1113212459618105000L
  - Serialized Fields
    - m_replaceMissing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing
```
      Filter to replace missing values
    - m_nominalToBinary
```
weka.filters.supervised.attribute.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.HoeffdingTree extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  7117521775722396251L
  - Serialized Fields
    - m_header
```
weka.core.Instances m_header
```
    - m_root
```
weka.classifiers.trees.ht.HNode m_root
```
    - m_gracePeriod
```
double m_gracePeriod
```
    - m_splitConfidence
```
double m_splitConfidence
```
    - m_hoeffdingTieThreshold
```
double m_hoeffdingTieThreshold
```
    - m_minFracWeightForTwoBranchesGain
```
double m_minFracWeightForTwoBranchesGain
```
    - m_selectedSplitMetric
```
int m_selectedSplitMetric
```
    - m_splitMetric
```
weka.classifiers.trees.ht.SplitMetric m_splitMetric
```
    - m_leafStrategy
```
int m_leafStrategy
```
    - m_nbThreshold
```
double m_nbThreshold
```
    - m_activeLeafCount
```
int m_activeLeafCount
```
    - m_inactiveLeafCount
```
int m_inactiveLeafCount
```
    - m_decisionNodeCount
```
int m_decisionNodeCount
```
    - m_printLeafModels
```
boolean m_printLeafModels
```
- Class weka.classifiers.trees.J48 extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -217733168393644444L
  - Serialized Fields
    - m_root
```
weka.classifiers.trees.j48.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
```
    - m_doNotMakeSplitPointActualValue
```
boolean m_doNotMakeSplitPointActualValue
```
- Class weka.classifiers.trees.LMT extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -1113212459618104943L
  - Serialized Fields
    - m_replaceMissing
```
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing
```
    - m_nominalToBinary
```
weka.filters.supervised.attribute.NominalToBinary m_nominalToBinary
```
    - m_tree
```
weka.classifiers.trees.lmt.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
```
    - m_doNotMakeSplitPointActualValue
```
boolean m_doNotMakeSplitPointActualValue
```
- Class weka.classifiers.trees.M5P extends weka.classifiers.trees.m5.M5Base implements Serializable
  
  serialVersionUID:
  
  -6118439039768244417L
- Class weka.classifiers.trees.RandomForest extends weka.classifiers.meta.Bagging implements Serializable
  
  serialVersionUID:
  
  1116839470751428698L
  - Serialized Fields
    - m_computeAttributeImportance
```
boolean m_computeAttributeImportance
```
- Class weka.classifiers.trees.RandomTree extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -9051119597407396024L
  - Serialized Fields
    - m_Tree
```
weka.classifiers.trees.RandomTree.Tree m_Tree
```
    - m_Info
```
weka.core.Instances m_Info
```
    - 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_BreakTiesRandomly
```
boolean m_BreakTiesRandomly
```
    - m_zeroR
```
weka.classifiers.Classifier m_zeroR
```
    - m_MinVarianceProp
```
double m_MinVarianceProp
```
    - m_computeImpurityDecreases
```
boolean m_computeImpurityDecreases
```
    - m_impurityDecreasees
```
double[][] m_impurityDecreasees
```
- Class weka.classifiers.trees.RandomTree.Tree extends java.lang.Object implements Serializable
  
  serialVersionUID:
  
  3549573538656522569L
  - Serialized Fields
    - m_Successors
```
weka.classifiers.trees.RandomTree.Tree[] m_Successors
```
    - m_Attribute
```
int m_Attribute
```
    - m_SplitPoint
```
double m_SplitPoint
```
    - m_Prop
```
double[] m_Prop
```
    - m_ClassDistribution
```
double[] m_ClassDistribution
```
    - m_Distribution
```
double[] m_Distribution
```
- Class weka.classifiers.trees.REPTree extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  -9216785998198681299L
  - Serialized Fields
    - m_zeroR
```
weka.classifiers.rules.ZeroR m_zeroR
```
    - m_Tree
```
weka.classifiers.trees.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
```
weka.core.Instances m_Info
```
    - m_Successors
```
weka.classifiers.trees.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
```
weka.classifiers.trees.j48.ModelSelection m_modelSelection
```
      ModelSelection object (for splitting)
    - m_nominalToBinary
```
weka.filters.supervised.attribute.NominalToBinary m_nominalToBinary
```
      Filter to convert nominal attributes to binary
    - m_higherRegressions
```
weka.classifiers.functions.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
```
weka.classifiers.trees.j48.ClassifierSplitModel m_localModel
```
      The ClassifierSplitModel (for splitting)
    - m_auxLocalModel
```
weka.classifiers.trees.j48.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
- Class weka.classifiers.trees.ft.LogisticBase extends weka.classifiers.AbstractClassifier implements Serializable
  
  serialVersionUID:
  
  168765678097825064L
  - Serialized Fields
    - m_numericDataHeader
```
weka.core.Instances m_numericDataHeader
```
      Header-only version of the numeric version of the training data
    - m_numericData
```
weka.core.Instances m_numericData
```
      Numeric version of the training data. Original class is replaced by a numeric pseudo-class.
    - m_train
```
weka.core.Instances m_train
```
      Training data
    - m_useCrossValidation
```
boolean m_useCrossValidation
```
      Use cross-validation to determine best number of LogitBoost iterations ?
    - m_errorOnProbabilities
```
boolean m_errorOnProbabilities
```
      Use error on probabilities for stopping criterion of LogitBoost?
    - m_fixedNumIterations
```
int m_fixedNumIterations
```
      Use fixed number of iterations for LogitBoost? (if negative, cross-validate number of iterations)
    - m_heuristicStop
```
int m_heuristicStop
```
      Use heuristic to stop performing LogitBoost iterations earlier? If enabled, LogitBoost is stopped if the current (local) minimum of the error on a test set as a function of the number of iterations has not changed for m_heuristicStop iterations.
    - m_numRegressions
```
int m_numRegressions
```
      The number of LogitBoost iterations performed.
    - m_maxIterations
```
int m_maxIterations
```
      The maximum number of LogitBoost iterations
    - m_numClasses
```
int m_numClasses
```
      The number of different classes
    - m_regressions
```
weka.classifiers.functions.SimpleLinearRegression[][] m_regressions
```
      Array holding the simple regression functions fit by LogitBoost
    - m_useAIC
```
boolean m_useAIC
```
      If true, the AIC is used to choose the best iteration
    - m_numParameters
```
double m_numParameters
```
      Effective number of parameters used for AIC / BIC automatic stopping
    - 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.

Serialized Form

Package weka.classifiers.trees

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

Serialized Fields

m_AttIndex

m_SplitPoint

m_Distribution

m_Instances

m_ZeroR

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

Serialized Fields

m_replaceMissing

m_nominalToBinary

m_tree

m_convertNominal

m_errorOnProbabilities

m_minNumInstances

m_numBoostingIterations

m_modelType

m_weightTrimBeta

m_useAIC

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

Serialized Fields

m_header

m_root

m_gracePeriod

m_splitConfidence

m_hoeffdingTieThreshold

m_minFracWeightForTwoBranchesGain

m_selectedSplitMetric

m_splitMetric

m_leafStrategy

m_nbThreshold

m_activeLeafCount

m_inactiveLeafCount

m_decisionNodeCount

m_printLeafModels

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

Serialized Fields

m_root

m_unpruned

m_collapseTree

m_CF

m_minNumObj

m_useMDLcorrection

m_useLaplace

m_reducedErrorPruning

m_numFolds

m_binarySplits

m_subtreeRaising

m_noCleanup

m_Seed

m_doNotMakeSplitPointActualValue

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

Serialized Fields

m_replaceMissing

m_nominalToBinary

m_tree

m_fastRegression

m_convertNominal

m_splitOnResiduals

m_errorOnProbabilities

m_minNumInstances

m_numBoostingIterations

m_weightTrimBeta

m_useAIC

m_doNotMakeSplitPointActualValue

Class weka.classifiers.trees.M5P extends weka.classifiers.trees.m5.M5Base implements Serializable

Class weka.classifiers.trees.RandomForest extends weka.classifiers.meta.Bagging implements Serializable

Serialized Fields

m_computeAttributeImportance

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

Serialized Fields

m_Tree

m_Info

m_MinNum

m_KValue

m_randomSeed

m_MaxDepth

m_NumFolds