ROOT   Reference Guide
TMVA::DecisionTree Class Reference

Implementation of a Decision Tree

In a decision tree successive decision nodes are used to categorize the events out of the sample as either signal or background. Each node uses only a single discriminating variable to decide if the event is signal-like ("goes right") or background-like ("goes left"). This forms a tree like structure with "baskets" at the end (leave nodes), and an event is classified as either signal or background according to whether the basket where it ends up has been classified signal or background during the training. Training of a decision tree is the process to define the "cut criteria" for each node. The training starts with the root node. Here one takes the full training event sample and selects the variable and corresponding cut value that gives the best separation between signal and background at this stage. Using this cut criterion, the sample is then divided into two subsamples, a signal-like (right) and a background-like (left) sample. Two new nodes are then created for each of the two sub-samples and they are constructed using the same mechanism as described for the root node. The devision is stopped once a certain node has reached either a minimum number of events, or a minimum or maximum signal purity. These leave nodes are then called "signal" or "background" if they contain more signal respective background events from the training sample.

Definition at line 65 of file DecisionTree.h.

## Public Types

enum  EPruneMethod { kExpectedErrorPruning =0, kCostComplexityPruning, kNoPruning }

typedef std::vector< const TMVA::Event * > EventConstList

typedef std::vector< TMVA::Event * > EventList

## Public Member Functions

DecisionTree (const DecisionTree &d)
copy constructor that creates a true copy, i.e. More...

DecisionTree (SeparationBase *sepType, Float_t minSize, Int_t nCuts, DataSetInfo *=NULL, UInt_t cls=0, Bool_t randomisedTree=kFALSE, Int_t useNvars=0, Bool_t usePoissonNvars=kFALSE, UInt_t nMaxDepth=9999999, Int_t iSeed=fgRandomSeed, Float_t purityLimit=0.5, Int_t treeID=0)
constructor specifying the separation type, the min number of events in a no that is still subjected to further splitting, the number of bins in the grid used in applying the cut for the node splitting. More...

DecisionTree (void)
default constructor using the GiniIndex as separation criterion, no restrictions on minium number of events in a leave note or the separation gain in the node splitting More...

virtual ~DecisionTree (void)
destructor More...

void ApplyValidationSample (const EventConstList *validationSample) const
run the validation sample through the (pruned) tree and fill in the nodes the variables NSValidation and NBValidadtion (i.e. More...

UInt_t BuildTree (const EventConstList &eventSample, DecisionTreeNode *node=NULL)
building the decision tree by recursively calling the splitting of one (root-) node into two daughter nodes (returns the number of nodes) More...

Double_t CheckEvent (const TMVA::Event *, Bool_t UseYesNoLeaf=kFALSE) const
the event e is put into the decision tree (starting at the root node) and the output is NodeType (signal) or (background) of the final node (basket) in which the given events ends up. More...

void CheckEventWithPrunedTree (const TMVA::Event *) const
pass a single validation event through a pruned decision tree on the way down the tree, fill in all the "intermediate" information that would normally be there from training. More...

virtual const char * ClassName () const

UInt_t CleanTree (DecisionTreeNode *node=NULL)
remove those last splits that result in two leaf nodes that are both of the type (i.e. More...

void ClearTree ()
clear the tree nodes (their S/N, Nevents etc), just keep the structure of the tree More...

UInt_t CountLeafNodes (TMVA::Node *n=NULL)
return the number of terminal nodes in the sub-tree below Node n More...

virtual DecisionTreeNodeCreateNode (UInt_t) const

virtual BinaryTreeCreateTree () const

void DescendTree (Node *n=NULL)
descend a tree to find all its leaf nodes More...

Bool_t DoRegression () const

void FillEvent (const TMVA::Event &event, TMVA::DecisionTreeNode *node)
fill the existing the decision tree structure by filling event in from the top node and see where they happen to end up More...

void FillTree (const EventList &eventSample)
fill the existing the decision tree structure by filling event in from the top node and see where they happen to end up More...

Types::EAnalysisType GetAnalysisType (void)

TMVA::DecisionTreeNodeGetEventNode (const TMVA::Event &e) const
get the pointer to the leaf node where a particular event ends up in... More...

std::vector< Double_tGetFisherCoefficients (const EventConstList &eventSample, UInt_t nFisherVars, UInt_t *mapVarInFisher)
calculate the fisher coefficients for the event sample and the variables used More...

Int_t GetNNodesBeforePruning ()

NodeGetNode (ULong_t sequence, UInt_t depth)
retrieve node from the tree. More...

Double_t GetNodePurityLimit () const

Double_t GetPruneStrength () const

void GetRandomisedVariables (Bool_t *useVariable, UInt_t *variableMap, UInt_t &nVars)

virtual DecisionTreeNodeGetRoot () const

Double_t GetSumWeights (const EventConstList *validationSample) const
calculate the normalization factor for a pruning validation sample More...

Int_t GetTreeID ()

std::vector< Double_tGetVariableImportance ()
Return the relative variable importance, normalized to all variables together having the importance 1. More...

Double_t GetVariableImportance (UInt_t ivar)
returns the relative importance of variable ivar More...

void PruneNode (TMVA::DecisionTreeNode *node)
prune away the subtree below the node More...

void PruneNodeInPlace (TMVA::DecisionTreeNode *node)
prune a node temporarily (without actually deleting its descendants which allows testing the pruned tree quality for many different pruning stages without "touching" the tree. More...

Double_t PruneTree (const EventConstList *validationSample=NULL)
prune (get rid of internal nodes) the Decision tree to avoid overtraining several different pruning methods can be applied as selected by the variable "fPruneMethod". More...

void SetAnalysisType (Types::EAnalysisType t)

void SetMinLinCorrForFisher (Double_t min)

void SetNodePurityLimit (Double_t p)

void SetNVars (Int_t n)

void SetParentTreeInNodes (Node *n=NULL)
descend a tree to find all its leaf nodes, fill max depth reached in the tree at the same time. More...

void SetPruneMethod (EPruneMethod m=kCostComplexityPruning)

void SetPruneStrength (Double_t p)

void SetTreeID (Int_t treeID)

void SetUseExclusiveVars (Bool_t t=kTRUE)

void SetUseFisherCuts (Bool_t t=kTRUE)

Double_t TestPrunedTreeQuality (const DecisionTreeNode *dt=NULL, Int_t mode=0) const
return the misclassification rate of a pruned tree a "pruned tree" may have set the variable "IsTerminal" to "arbitrary" at any node, hence this tree quality testing will stop there, hence test the pruned tree (while the full tree is still in place for normal/later use) More...

Double_t TrainNode (const EventConstList &eventSample, DecisionTreeNode *node)

Double_t TrainNodeFast (const EventConstList &eventSample, DecisionTreeNode *node)
Decide how to split a node using one of the variables that gives the best separation of signal/background. More...

Double_t TrainNodeFull (const EventConstList &eventSample, DecisionTreeNode *node)
train a node by finding the single optimal cut for a single variable that best separates signal and background (maximizes the separation gain) More...

Public Member Functions inherited from TMVA::BinaryTree
BinaryTree (void)
constructor for a yet "empty" tree. Needs to be filled afterwards More...

virtual ~BinaryTree ()
destructor (deletes the nodes and "events" if owned by the tree More...

UInt_t CountNodes (Node *n=NULL)
return the number of nodes in the tree. (make a new count --> takes time) More...

NodeGetLeftDaughter (Node *n)
get left daughter node current node "n" More...

UInt_t GetNNodes () const

NodeGetRightDaughter (Node *n)
get right daughter node current node "n" More...

UInt_t GetTotalTreeDepth () const

virtual void Print (std::ostream &os) const
recursively print the tree More...

virtual void Read (std::istream &istr, UInt_t tmva_Version_Code=TMVA_VERSION_CODE)
Read the binary tree from an input stream. More...

virtual void ReadXML (void *node, UInt_t tmva_Version_Code=TMVA_VERSION_CODE)

void SetRoot (Node *r)

void SetTotalTreeDepth (Int_t depth)

void SetTotalTreeDepth (Node *n=NULL)
descend a tree to find all its leaf nodes, fill max depth reached in the tree at the same time. More...

## Static Public Member Functions

static DecisionTreeCreateFromXML (void *node, UInt_t tmva_Version_Code=TMVA_VERSION_CODE)
re-create a new tree (decision tree or search tree) from XML More...

## Private Member Functions

Double_t SamplePurity (EventList eventSample)
calculates the purity S/(S+B) of a given event sample More...

## Private Attributes

Types::EAnalysisType fAnalysisType

DataSetInfofDataSetInfo

UInt_t fMaxDepth

Double_t fMinLinCorrForFisher

Double_t fMinNodeSize

Double_t fMinSepGain

Double_t fMinSize

TRandom3fMyTrandom

Int_t fNCuts

Int_t fNNodesBeforePruning

Double_t fNodePurityLimit

UInt_t fNvars

EPruneMethod fPruneMethod

Double_t fPruneStrength

Bool_t fRandomisedTree

RegressionVariancefRegType

SeparationBasefSepType

UInt_t fSigClass

Int_t fTreeID

Bool_t fUseExclusiveVars

Bool_t fUseFisherCuts

Int_t fUseNvars

Bool_t fUsePoissonNvars

Bool_t fUseSearchTree

std::vector< Double_tfVariableImportance

## Static Private Attributes

static const Int_t fgDebugLevel = 0

static const Int_t fgRandomSeed = 0

Protected Member Functions inherited from TMVA::BinaryTree
void DeleteNode (Node *)
protected, recursive, function used by the class destructor and when Pruning More...

MsgLoggerLog () const

Protected Attributes inherited from TMVA::BinaryTree
UInt_t fDepth

UInt_t fNNodes

NodefRoot

#include <TMVA/DecisionTree.h>

Inheritance diagram for TMVA::DecisionTree:
[legend]

## ◆ EventConstList

 typedef std::vector TMVA::DecisionTree::EventConstList

Definition at line 74 of file DecisionTree.h.

## ◆ EventList

 typedef std::vector TMVA::DecisionTree::EventList

Definition at line 73 of file DecisionTree.h.

## ◆ EPruneMethod

Enumerator
kExpectedErrorPruning
kCostComplexityPruning
kNoPruning

Definition at line 139 of file DecisionTree.h.

## ◆ DecisionTree() [1/3]

 TMVA::DecisionTree::DecisionTree ( void )

default constructor using the GiniIndex as separation criterion, no restrictions on minium number of events in a leave note or the separation gain in the node splitting

Definition at line 115 of file DecisionTree.cxx.

## ◆ DecisionTree() [2/3]

 TMVA::DecisionTree::DecisionTree ( TMVA::SeparationBase * sepType, Float_t minSize, Int_t nCuts, DataSetInfo * dataInfo = NULL, UInt_t cls = 0, Bool_t randomisedTree = kFALSE, Int_t useNvars = 0, Bool_t usePoissonNvars = kFALSE, UInt_t nMaxDepth = 9999999, Int_t iSeed = fgRandomSeed, Float_t purityLimit = 0.5, Int_t treeID = 0 )

constructor specifying the separation type, the min number of events in a no that is still subjected to further splitting, the number of bins in the grid used in applying the cut for the node splitting.

Definition at line 150 of file DecisionTree.cxx.

## ◆ DecisionTree() [3/3]

 TMVA::DecisionTree::DecisionTree ( const DecisionTree & d )

copy constructor that creates a true copy, i.e.

a completely independent tree the node copy will recursively copy all the nodes

Definition at line 200 of file DecisionTree.cxx.

## ◆ ~DecisionTree()

 TMVA::DecisionTree::~DecisionTree ( void )
virtual

destructor

Definition at line 236 of file DecisionTree.cxx.

## ◆ ApplyValidationSample()

 void TMVA::DecisionTree::ApplyValidationSample ( const EventConstList * validationSample ) const

run the validation sample through the (pruned) tree and fill in the nodes the variables NSValidation and NBValidadtion (i.e.

how many of the Signal and Background events from the validation sample. This is then later used when asking for the "tree quality" ..

Definition at line 1029 of file DecisionTree.cxx.

## ◆ BuildTree()

 UInt_t TMVA::DecisionTree::BuildTree ( const EventConstList & eventSample, DecisionTreeNode * node = NULL )

building the decision tree by recursively calling the splitting of one (root-) node into two daughter nodes (returns the number of nodes)

Definition at line 377 of file DecisionTree.cxx.

## ◆ CheckEvent()

 Double_t TMVA::DecisionTree::CheckEvent ( const TMVA::Event * e, Bool_t UseYesNoLeaf = kFALSE ) const

the event e is put into the decision tree (starting at the root node) and the output is NodeType (signal) or (background) of the final node (basket) in which the given events ends up.

I.e. the result of the classification if the event for this decision tree.

Definition at line 2690 of file DecisionTree.cxx.

## ◆ CheckEventWithPrunedTree()

 void TMVA::DecisionTree::CheckEventWithPrunedTree ( const TMVA::Event * e ) const

pass a single validation event through a pruned decision tree on the way down the tree, fill in all the "intermediate" information that would normally be there from training.

Definition at line 1085 of file DecisionTree.cxx.

## ◆ ClassName()

 virtual const char* TMVA::DecisionTree::ClassName ( ) const
inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 98 of file DecisionTree.h.

## ◆ CleanTree()

 UInt_t TMVA::DecisionTree::CleanTree ( DecisionTreeNode * node = NULL )

remove those last splits that result in two leaf nodes that are both of the type (i.e.

both signal or both background) this of course is only a reasonable thing to do when you use "YesOrNo" leafs, while it might loose s.th. if you use the purity information in the nodes. --> hence I don't call it automatically in the tree building

Definition at line 937 of file DecisionTree.cxx.

## ◆ ClearTree()

 void TMVA::DecisionTree::ClearTree ( )

clear the tree nodes (their S/N, Nevents etc), just keep the structure of the tree

Definition at line 923 of file DecisionTree.cxx.

## ◆ CountLeafNodes()

 UInt_t TMVA::DecisionTree::CountLeafNodes ( TMVA::Node * n = NULL )

return the number of terminal nodes in the sub-tree below Node n

Definition at line 1131 of file DecisionTree.cxx.

## ◆ CreateFromXML()

 TMVA::DecisionTree * TMVA::DecisionTree::CreateFromXML ( void * node, UInt_t tmva_Version_Code = TMVA_VERSION_CODE )
static

re-create a new tree (decision tree or search tree) from XML

Definition at line 281 of file DecisionTree.cxx.

## ◆ CreateNode()

 virtual DecisionTreeNode* TMVA::DecisionTree::CreateNode ( UInt_t ) const
inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 95 of file DecisionTree.h.

## ◆ CreateTree()

 virtual BinaryTree* TMVA::DecisionTree::CreateTree ( ) const
inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 96 of file DecisionTree.h.

## ◆ DescendTree()

 void TMVA::DecisionTree::DescendTree ( Node * n = NULL )

descend a tree to find all its leaf nodes

Definition at line 1160 of file DecisionTree.cxx.

## ◆ DoRegression()

 Bool_t TMVA::DecisionTree::DoRegression ( ) const
inline

Definition at line 188 of file DecisionTree.h.

## ◆ FillEvent()

 void TMVA::DecisionTree::FillEvent ( const TMVA::Event & event, TMVA::DecisionTreeNode * node )

fill the existing the decision tree structure by filling event in from the top node and see where they happen to end up

Definition at line 891 of file DecisionTree.cxx.

## ◆ FillTree()

 void TMVA::DecisionTree::FillTree ( const EventList & eventSample )

fill the existing the decision tree structure by filling event in from the top node and see where they happen to end up

Definition at line 880 of file DecisionTree.cxx.

## ◆ GetAnalysisType()

 Types::EAnalysisType TMVA::DecisionTree::GetAnalysisType ( void )
inline

Definition at line 190 of file DecisionTree.h.

## ◆ GetEventNode()

 TMVA::DecisionTreeNode * TMVA::DecisionTree::GetEventNode ( const TMVA::Event & e ) const

get the pointer to the leaf node where a particular event ends up in...

Definition at line 2673 of file DecisionTree.cxx.

## ◆ GetFisherCoefficients()

 std::vector< Double_t > TMVA::DecisionTree::GetFisherCoefficients ( const EventConstList & eventSample, UInt_t nFisherVars, UInt_t * mapVarInFisher )

calculate the fisher coefficients for the event sample and the variables used

Definition at line 2342 of file DecisionTree.cxx.

## ◆ GetNNodesBeforePruning()

 Int_t TMVA::DecisionTree::GetNNodesBeforePruning ( )
inline

Definition at line 180 of file DecisionTree.h.

## ◆ GetNode()

 TMVA::Node * TMVA::DecisionTree::GetNode ( ULong_t sequence, UInt_t depth )

retrieve node from the tree.

Its position (up to a maximal tree depth of 64) is coded as a sequence of left-right moves starting from the root, coded as 0-1 bit patterns stored in the "long-integer" (i.e. 0:left ; 1:right

Definition at line 1231 of file DecisionTree.cxx.

## ◆ GetNodePurityLimit()

 Double_t TMVA::DecisionTree::GetNodePurityLimit ( ) const
inline

Definition at line 162 of file DecisionTree.h.

## ◆ GetPruneStrength()

 Double_t TMVA::DecisionTree::GetPruneStrength ( ) const
inline

Definition at line 147 of file DecisionTree.h.

## ◆ GetRandomisedVariables()

 void TMVA::DecisionTree::GetRandomisedVariables ( Bool_t * useVariable, UInt_t * variableMap, UInt_t & nVars )

Definition at line 1247 of file DecisionTree.cxx.

## ◆ GetRoot()

 virtual DecisionTreeNode* TMVA::DecisionTree::GetRoot ( ) const
inlinevirtual

Reimplemented from TMVA::BinaryTree.

Definition at line 94 of file DecisionTree.h.

## ◆ GetSumWeights()

 Double_t TMVA::DecisionTree::GetSumWeights ( const EventConstList * validationSample ) const

calculate the normalization factor for a pruning validation sample

Definition at line 1118 of file DecisionTree.cxx.

## ◆ GetTreeID()

 Int_t TMVA::DecisionTree::GetTreeID ( )
inline

Definition at line 186 of file DecisionTree.h.

## ◆ GetVariableImportance() [1/2]

 vector< Double_t > TMVA::DecisionTree::GetVariableImportance ( )

Return the relative variable importance, normalized to all variables together having the importance 1.

The importance in evaluated as the total separation-gain that this variable had in the decision trees (weighted by the number of events)

Definition at line 2745 of file DecisionTree.cxx.

## ◆ GetVariableImportance() [2/2]

 Double_t TMVA::DecisionTree::GetVariableImportance ( UInt_t ivar )

returns the relative importance of variable ivar

Definition at line 2766 of file DecisionTree.cxx.

## ◆ PruneNode()

 void TMVA::DecisionTree::PruneNode ( TMVA::DecisionTreeNode * node )

prune away the subtree below the node

Definition at line 1194 of file DecisionTree.cxx.

## ◆ PruneNodeInPlace()

 void TMVA::DecisionTree::PruneNodeInPlace ( TMVA::DecisionTreeNode * node )

prune a node temporarily (without actually deleting its descendants which allows testing the pruned tree quality for many different pruning stages without "touching" the tree.

Definition at line 1217 of file DecisionTree.cxx.

## ◆ PruneTree()

 Double_t TMVA::DecisionTree::PruneTree ( const EventConstList * validationSample = NULL )

prune (get rid of internal nodes) the Decision tree to avoid overtraining several different pruning methods can be applied as selected by the variable "fPruneMethod".

Definition at line 964 of file DecisionTree.cxx.

## ◆ SamplePurity()

 Double_t TMVA::DecisionTree::SamplePurity ( EventList eventSample )
private

calculates the purity S/(S+B) of a given event sample

Definition at line 2722 of file DecisionTree.cxx.

## ◆ SetAnalysisType()

 void TMVA::DecisionTree::SetAnalysisType ( Types::EAnalysisType t )
inline

Definition at line 189 of file DecisionTree.h.

## ◆ SetMinLinCorrForFisher()

 void TMVA::DecisionTree::SetMinLinCorrForFisher ( Double_t min )
inline

Definition at line 192 of file DecisionTree.h.

## ◆ SetNodePurityLimit()

 void TMVA::DecisionTree::SetNodePurityLimit ( Double_t p )
inline

Definition at line 161 of file DecisionTree.h.

## ◆ SetNVars()

 void TMVA::DecisionTree::SetNVars ( Int_t n )
inline

Definition at line 194 of file DecisionTree.h.

## ◆ SetParentTreeInNodes()

 void TMVA::DecisionTree::SetParentTreeInNodes ( Node * n = NULL )

descend a tree to find all its leaf nodes, fill max depth reached in the tree at the same time.

Definition at line 248 of file DecisionTree.cxx.

## ◆ SetPruneMethod()

 void TMVA::DecisionTree::SetPruneMethod ( EPruneMethod m = kCostComplexityPruning )
inline

Definition at line 140 of file DecisionTree.h.

## ◆ SetPruneStrength()

 void TMVA::DecisionTree::SetPruneStrength ( Double_t p )
inline

Definition at line 146 of file DecisionTree.h.

## ◆ SetTreeID()

 void TMVA::DecisionTree::SetTreeID ( Int_t treeID )
inline

Definition at line 185 of file DecisionTree.h.

## ◆ SetUseExclusiveVars()

 void TMVA::DecisionTree::SetUseExclusiveVars ( Bool_t t = kTRUE )
inline

Definition at line 193 of file DecisionTree.h.

## ◆ SetUseFisherCuts()

 void TMVA::DecisionTree::SetUseFisherCuts ( Bool_t t = kTRUE )
inline

Definition at line 191 of file DecisionTree.h.

## ◆ TestPrunedTreeQuality()

 Double_t TMVA::DecisionTree::TestPrunedTreeQuality ( const DecisionTreeNode * dt = NULL, Int_t mode = 0 ) const

return the misclassification rate of a pruned tree a "pruned tree" may have set the variable "IsTerminal" to "arbitrary" at any node, hence this tree quality testing will stop there, hence test the pruned tree (while the full tree is still in place for normal/later use)

Definition at line 1043 of file DecisionTree.cxx.

## ◆ TrainNode()

 Double_t TMVA::DecisionTree::TrainNode ( const EventConstList & eventSample, DecisionTreeNode * node )
inline

Definition at line 108 of file DecisionTree.h.

## ◆ TrainNodeFast()

 Double_t TMVA::DecisionTree::TrainNodeFast ( const EventConstList & eventSample, TMVA::DecisionTreeNode * node )

Decide how to split a node using one of the variables that gives the best separation of signal/background.

In order to do this, for each variable a scan of the different cut values in a grid (grid = fNCuts) is performed and the resulting separation gains are compared. in addition to the individual variables, one can also ask for a fisher discriminant being built out of (some) of the variables and used as a possible multivariate split.

Definition at line 1374 of file DecisionTree.cxx.

## ◆ TrainNodeFull()

 Double_t TMVA::DecisionTree::TrainNodeFull ( const EventConstList & eventSample, TMVA::DecisionTreeNode * node )

train a node by finding the single optimal cut for a single variable that best separates signal and background (maximizes the separation gain)

Definition at line 2536 of file DecisionTree.cxx.

## ◆ fAnalysisType

 Types::EAnalysisType TMVA::DecisionTree::fAnalysisType
private

Definition at line 239 of file DecisionTree.h.

## ◆ fDataSetInfo

 DataSetInfo* TMVA::DecisionTree::fDataSetInfo
private

Definition at line 241 of file DecisionTree.h.

## ◆ fgDebugLevel

 const Int_t TMVA::DecisionTree::fgDebugLevel = 0
staticprivate

Definition at line 236 of file DecisionTree.h.

## ◆ fgRandomSeed

 const Int_t TMVA::DecisionTree::fgRandomSeed = 0
staticprivate

Definition at line 69 of file DecisionTree.h.

## ◆ fMaxDepth

 UInt_t TMVA::DecisionTree::fMaxDepth
private

Definition at line 234 of file DecisionTree.h.

## ◆ fMinLinCorrForFisher

 Double_t TMVA::DecisionTree::fMinLinCorrForFisher
private

Definition at line 208 of file DecisionTree.h.

## ◆ fMinNodeSize

 Double_t TMVA::DecisionTree::fMinNodeSize
private

Definition at line 215 of file DecisionTree.h.

## ◆ fMinSepGain

 Double_t TMVA::DecisionTree::fMinSepGain
private

Definition at line 216 of file DecisionTree.h.

## ◆ fMinSize

 Double_t TMVA::DecisionTree::fMinSize
private

Definition at line 214 of file DecisionTree.h.

## ◆ fMyTrandom

 TRandom3* TMVA::DecisionTree::fMyTrandom
private

Definition at line 230 of file DecisionTree.h.

## ◆ fNCuts

 Int_t TMVA::DecisionTree::fNCuts
private

Definition at line 206 of file DecisionTree.h.

## ◆ fNNodesBeforePruning

 Int_t TMVA::DecisionTree::fNNodesBeforePruning
private

Definition at line 222 of file DecisionTree.h.

## ◆ fNodePurityLimit

 Double_t TMVA::DecisionTree::fNodePurityLimit
private

Definition at line 224 of file DecisionTree.h.

## ◆ fNvars

 UInt_t TMVA::DecisionTree::fNvars
private

Definition at line 205 of file DecisionTree.h.

## ◆ fPruneMethod

 EPruneMethod TMVA::DecisionTree::fPruneMethod
private

Definition at line 221 of file DecisionTree.h.

## ◆ fPruneStrength

 Double_t TMVA::DecisionTree::fPruneStrength
private

Definition at line 219 of file DecisionTree.h.

## ◆ fRandomisedTree

 Bool_t TMVA::DecisionTree::fRandomisedTree
private

Definition at line 226 of file DecisionTree.h.

## ◆ fRegType

 RegressionVariance* TMVA::DecisionTree::fRegType
private

Definition at line 212 of file DecisionTree.h.

## ◆ fSepType

 SeparationBase* TMVA::DecisionTree::fSepType
private

Definition at line 211 of file DecisionTree.h.

## ◆ fSigClass

 UInt_t TMVA::DecisionTree::fSigClass
private

Definition at line 235 of file DecisionTree.h.

## ◆ fTreeID

 Int_t TMVA::DecisionTree::fTreeID
private

Definition at line 237 of file DecisionTree.h.

## ◆ fUseExclusiveVars

 Bool_t TMVA::DecisionTree::fUseExclusiveVars
private

Definition at line 209 of file DecisionTree.h.

## ◆ fUseFisherCuts

 Bool_t TMVA::DecisionTree::fUseFisherCuts
private

Definition at line 207 of file DecisionTree.h.

## ◆ fUseNvars

 Int_t TMVA::DecisionTree::fUseNvars
private

Definition at line 227 of file DecisionTree.h.

## ◆ fUsePoissonNvars

 Bool_t TMVA::DecisionTree::fUsePoissonNvars
private

Definition at line 228 of file DecisionTree.h.

## ◆ fUseSearchTree

 Bool_t TMVA::DecisionTree::fUseSearchTree
private

Definition at line 218 of file DecisionTree.h.

## ◆ fVariableImportance

 std::vector< Double_t > TMVA::DecisionTree::fVariableImportance
private

Definition at line 232 of file DecisionTree.h.

Libraries for TMVA::DecisionTree:
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The documentation for this class was generated from the following files: