Definition at line 73 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 (void)

	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 (const DecisionTree &d)
	copy constructor that creates a true copy, i.e. 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 throught 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 DecisionTreeNode *	CreateNode (UInt_t) const

virtual BinaryTree *	CreateTree () 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)

Types::EAnalysisType	GetAnalysisType (void)

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

std::vector< Double_t >	GetFisherCoefficients (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 ()

Node *	GetNode (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 DecisionTreeNode *	GetRoot () const

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

Int_t	GetTreeID ()

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

Double_t	GetVariableImportance (UInt_t ivar)
	returns the relative improtance 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 temporaily (without actually deleting its decendants 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 serveral 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)

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...

virtual void *	AddXMLTo (void *parent) const
	add attributes to XML More...

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

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

UInt_t	GetNNodes () const

Node *	GetRightDaughter (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)
	read attributes from XML More...

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 DecisionTree *	CreateFromXML (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...

Static Private Attributes
static const Int_t	fgDebugLevel = 0

static const Int_t	fgRandomSeed = 0

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

MsgLogger &	Log () const

Protected Attributes inherited from TMVA::BinaryTree
UInt_t	fDepth

UInt_t	fNNodes

Node *	fRoot

#include <TMVA/DecisionTree.h>

Inheritance diagram for TMVA::DecisionTree:

[legend]

Member Typedef Documentation

◆ EventConstList

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

Definition at line 82 of file DecisionTree.h.

◆ EventList

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

Definition at line 81 of file DecisionTree.h.

Member Enumeration Documentation

◆ EPruneMethod

enum TMVA::DecisionTree::EPruneMethod

Enumerator
kExpectedErrorPruning
kCostComplexityPruning
kNoPruning

Definition at line 147 of file DecisionTree.h.

Constructor & Destructor Documentation

◆ DecisionTree() [1/3]

TMVA::DecisionTree::DecisionTree ( void )

Definition at line 114 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 149 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 199 of file DecisionTree.cxx.

◆ ~DecisionTree()

TMVA::DecisionTree::~DecisionTree ( void )

virtual

destructor

Definition at line 234 of file DecisionTree.cxx.

Member Function Documentation

◆ 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 686 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 293 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 1712 of file DecisionTree.cxx.

◆ CheckEventWithPrunedTree()

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

pass a single validation event throught 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 742 of file DecisionTree.cxx.

◆ ClassName()

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

inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 106 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 590 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 576 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 790 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 279 of file DecisionTree.cxx.

◆ CreateNode()

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

inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 103 of file DecisionTree.h.

◆ CreateTree()

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

inlinevirtual

Implements TMVA::BinaryTree.

Definition at line 104 of file DecisionTree.h.

◆ DescendTree()

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

descend a tree to find all its leaf nodes

Definition at line 819 of file DecisionTree.cxx.

◆ DoRegression()

Bool_t TMVA::DecisionTree::DoRegression ( ) const

inline

Definition at line 196 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 542 of file DecisionTree.cxx.

◆ FillTree()

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

Definition at line 528 of file DecisionTree.cxx.

◆ GetAnalysisType()

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

inline

Definition at line 198 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...

(used in gradient boosting)

Definition at line 1695 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 1369 of file DecisionTree.cxx.

◆ GetNNodesBeforePruning()

Int_t TMVA::DecisionTree::GetNNodesBeforePruning ( )

inline

Definition at line 188 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 890 of file DecisionTree.cxx.

◆ GetNodePurityLimit()

Double_t TMVA::DecisionTree::GetNodePurityLimit ( ) const

inline

Definition at line 170 of file DecisionTree.h.

◆ GetPruneStrength()

Double_t TMVA::DecisionTree::GetPruneStrength ( ) const

inline

Definition at line 155 of file DecisionTree.h.

◆ GetRandomisedVariables()

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

Definition at line 907 of file DecisionTree.cxx.

◆ GetRoot()

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

inlinevirtual

Reimplemented from TMVA::BinaryTree.

Definition at line 102 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 775 of file DecisionTree.cxx.

◆ GetTreeID()

Int_t TMVA::DecisionTree::GetTreeID ( )

inline

Definition at line 194 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 1765 of file DecisionTree.cxx.

◆ GetVariableImportance() [2/2]

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

returns the relative improtance of variable ivar

Definition at line 1786 of file DecisionTree.cxx.

◆ PruneNode()

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

prune away the subtree below the node

Definition at line 853 of file DecisionTree.cxx.

◆ PruneNodeInPlace()

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

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

Definition at line 876 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 serveral different pruning methods can be applied as selected by the variable "fPruneMethod".

Definition at line 617 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 1742 of file DecisionTree.cxx.

◆ SetAnalysisType()

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

inline

Definition at line 197 of file DecisionTree.h.

◆ SetMinLinCorrForFisher()

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

inline

Definition at line 200 of file DecisionTree.h.

◆ SetNodePurityLimit()

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

inline

Definition at line 169 of file DecisionTree.h.

◆ SetNVars()

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

inline

Definition at line 202 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 246 of file DecisionTree.cxx.

◆ SetPruneMethod()

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

inline

Definition at line 148 of file DecisionTree.h.

◆ SetPruneStrength()

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

inline

Definition at line 154 of file DecisionTree.h.

◆ SetTreeID()

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

inline

Definition at line 193 of file DecisionTree.h.

◆ SetUseExclusiveVars()

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

inline

Definition at line 201 of file DecisionTree.h.

◆ SetUseFisherCuts()

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

inline

Definition at line 199 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 700 of file DecisionTree.cxx.

◆ TrainNode()

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

inline

Definition at line 116 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 940 of file DecisionTree.cxx.

◆ TrainNodeFull()

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

Definition at line 1561 of file DecisionTree.cxx.

Member Data Documentation

◆ fAnalysisType

Types::EAnalysisType TMVA::DecisionTree::fAnalysisType

private

Definition at line 248 of file DecisionTree.h.

◆ fDataSetInfo

DataSetInfo* TMVA::DecisionTree::fDataSetInfo

private

Definition at line 250 of file DecisionTree.h.

◆ fgDebugLevel

const Int_t TMVA::DecisionTree::fgDebugLevel = 0

staticprivate

Definition at line 245 of file DecisionTree.h.

◆ fgRandomSeed

const Int_t TMVA::DecisionTree::fgRandomSeed = 0

staticprivate

Definition at line 77 of file DecisionTree.h.

◆ fMaxDepth

UInt_t TMVA::DecisionTree::fMaxDepth

private

Definition at line 243 of file DecisionTree.h.

◆ fMinLinCorrForFisher

Double_t TMVA::DecisionTree::fMinLinCorrForFisher

private

Definition at line 217 of file DecisionTree.h.

◆ fMinNodeSize

Double_t TMVA::DecisionTree::fMinNodeSize

private

Definition at line 224 of file DecisionTree.h.

◆ fMinSepGain

Double_t TMVA::DecisionTree::fMinSepGain

private

Definition at line 225 of file DecisionTree.h.

◆ fMinSize

Double_t TMVA::DecisionTree::fMinSize

private

Definition at line 223 of file DecisionTree.h.

◆ fMyTrandom

TRandom3* TMVA::DecisionTree::fMyTrandom

private

Definition at line 239 of file DecisionTree.h.

◆ fNCuts

Int_t TMVA::DecisionTree::fNCuts

private

Definition at line 215 of file DecisionTree.h.

◆ fNNodesBeforePruning

Int_t TMVA::DecisionTree::fNNodesBeforePruning

private

Definition at line 231 of file DecisionTree.h.

◆ fNodePurityLimit

Double_t TMVA::DecisionTree::fNodePurityLimit

private

Definition at line 233 of file DecisionTree.h.

◆ fNvars

UInt_t TMVA::DecisionTree::fNvars

private

Definition at line 214 of file DecisionTree.h.

◆ fPruneMethod

EPruneMethod TMVA::DecisionTree::fPruneMethod

private

Definition at line 230 of file DecisionTree.h.

◆ fPruneStrength

Double_t TMVA::DecisionTree::fPruneStrength

private

Definition at line 228 of file DecisionTree.h.

◆ fRandomisedTree

Bool_t TMVA::DecisionTree::fRandomisedTree

private

Definition at line 235 of file DecisionTree.h.

◆ fRegType

RegressionVariance* TMVA::DecisionTree::fRegType

private

Definition at line 221 of file DecisionTree.h.

◆ fSepType

SeparationBase* TMVA::DecisionTree::fSepType

private

Definition at line 220 of file DecisionTree.h.

◆ fSigClass

UInt_t TMVA::DecisionTree::fSigClass

private

Definition at line 244 of file DecisionTree.h.

◆ fTreeID

Int_t TMVA::DecisionTree::fTreeID

private

Definition at line 246 of file DecisionTree.h.

◆ fUseExclusiveVars

Bool_t TMVA::DecisionTree::fUseExclusiveVars

private

Definition at line 218 of file DecisionTree.h.

◆ fUseFisherCuts

Bool_t TMVA::DecisionTree::fUseFisherCuts

private

Definition at line 216 of file DecisionTree.h.

◆ fUseNvars

Int_t TMVA::DecisionTree::fUseNvars

private

Definition at line 236 of file DecisionTree.h.

◆ fUsePoissonNvars

Bool_t TMVA::DecisionTree::fUsePoissonNvars

private

Definition at line 237 of file DecisionTree.h.

◆ fUseSearchTree

Bool_t TMVA::DecisionTree::fUseSearchTree

private

Definition at line 227 of file DecisionTree.h.

◆ fVariableImportance

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

private

Definition at line 241 of file DecisionTree.h.

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

tmva/tmva/inc/TMVA/DecisionTree.h
tmva/tmva/src/DecisionTree.cxx

Private Attributes
Types::EAnalysisType	fAnalysisType

DataSetInfo *	fDataSetInfo

UInt_t	fMaxDepth

Double_t	fMinLinCorrForFisher

Double_t	fMinNodeSize

Double_t	fMinSepGain

Double_t	fMinSize

TRandom3 *	fMyTrandom

Int_t	fNCuts

Int_t	fNNodesBeforePruning

Double_t	fNodePurityLimit

UInt_t	fNvars

EPruneMethod	fPruneMethod

Double_t	fPruneStrength

Bool_t	fRandomisedTree

RegressionVariance *	fRegType

SeparationBase *	fSepType

UInt_t	fSigClass

Int_t	fTreeID

Bool_t	fUseExclusiveVars

Bool_t	fUseFisherCuts

Int_t	fUseNvars

Bool_t	fUsePoissonNvars

Bool_t	fUseSearchTree

std::vector< Double_t >	fVariableImportance

Public Types

Public Member Functions

Static Public Member Functions

Private Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Member Typedef Documentation

◆ EventConstList

◆ EventList

Member Enumeration Documentation

◆ EPruneMethod

Constructor & Destructor Documentation

◆ DecisionTree() [1/3]

◆ DecisionTree() [2/3]

◆ DecisionTree() [3/3]

◆ ~DecisionTree()

Member Function Documentation

◆ ApplyValidationSample()

◆ BuildTree()

◆ CheckEvent()

◆ CheckEventWithPrunedTree()

◆ ClassName()

◆ CleanTree()

◆ ClearTree()

◆ CountLeafNodes()

◆ CreateFromXML()

◆ CreateNode()

◆ CreateTree()

◆ DescendTree()

◆ DoRegression()

◆ FillEvent()

◆ FillTree()

◆ GetAnalysisType()

◆ GetEventNode()

◆ GetFisherCoefficients()

◆ GetNNodesBeforePruning()

◆ GetNode()

◆ GetNodePurityLimit()

◆ GetPruneStrength()

◆ GetRandomisedVariables()

◆ GetRoot()

◆ GetSumWeights()

◆ GetTreeID()

◆ GetVariableImportance() [1/2]

◆ GetVariableImportance() [2/2]

◆ PruneNode()

◆ PruneNodeInPlace()

◆ PruneTree()

◆ SamplePurity()

◆ SetAnalysisType()

◆ SetMinLinCorrForFisher()

◆ SetNodePurityLimit()

◆ SetNVars()

◆ SetParentTreeInNodes()

◆ SetPruneMethod()

◆ SetPruneStrength()

◆ SetTreeID()

◆ SetUseExclusiveVars()

◆ SetUseFisherCuts()

◆ TestPrunedTreeQuality()

◆ TrainNode()

◆ TrainNodeFast()

◆ TrainNodeFull()

Member Data Documentation

◆ fAnalysisType

◆ fDataSetInfo

◆ fgDebugLevel

◆ fgRandomSeed

◆ fMaxDepth

◆ fMinLinCorrForFisher

◆ fMinNodeSize

◆ fMinSepGain

◆ fMinSize

◆ fMyTrandom

◆ fNCuts

◆ fNNodesBeforePruning

◆ fNodePurityLimit

◆ fNvars

◆ fPruneMethod