Definition at line 59 of file ExpectedErrorPruneTool.h.

Public Member Functions
	ExpectedErrorPruneTool ()

virtual	~ExpectedErrorPruneTool ()

virtual PruningInfo *	CalculatePruningInfo (DecisionTree dt, const IPruneTool::EventSample testEvents=NULL, Bool_t isAutomatic=kFALSE)

void	SetPruneStrengthIncrement (Double_t dalpha)

Public Member Functions inherited from TMVA::IPruneTool
	IPruneTool ()

virtual	~IPruneTool ()

void	SetPruneStrength (Double_t alpha)

Double_t	GetPruneStrength () const

void	SetAutomatic ()

Bool_t	IsAutomatic () const

Private Member Functions
void	FindListOfNodes (DecisionTreeNode *node)
	recursive pruning of nodes using the Expected Error Pruning (EEP) More...

Double_t	GetNodeError (DecisionTreeNode *node) const
	Calculate an UPPER limit on the error made by the classification done by this node. More...

Double_t	GetSubTreeError (DecisionTreeNode *node) const
	calculate the expected statistical error on the subtree below "node" which is used in the expected error pruning More...

Int_t	CountNodes (DecisionTreeNode *node, Int_t icount=0)

MsgLogger &	Log () const

Private Attributes
Double_t	fDeltaPruneStrength

Double_t	fNodePurityLimit
	the stepsize for optimizing the pruning strength parameter More...

std::vector< DecisionTreeNode * >	fPruneSequence
	the purity limit for labelling a terminal node as signal More...

MsgLogger *	fLogger
	the (optimal) prune sequence More...

Additional Inherited Members
Public Types inherited from TMVA::IPruneTool
typedef std::vector< const Event * >	EventSample

Protected Attributes inherited from TMVA::IPruneTool
Double_t	fPruneStrength

Double_t	S
	regularization parameter in pruning More...

Double_t	B

#include <TMVA/ExpectedErrorPruneTool.h>

Inheritance diagram for TMVA::ExpectedErrorPruneTool:

Collaboration diagram for TMVA::ExpectedErrorPruneTool:

Constructor & Destructor Documentation

§ ExpectedErrorPruneTool()

TMVA::ExpectedErrorPruneTool::ExpectedErrorPruneTool ( )

Definition at line 34 of file ExpectedErrorPruneTool.cxx.

§ ~ExpectedErrorPruneTool()

TMVA::ExpectedErrorPruneTool::~ExpectedErrorPruneTool ( )

virtual

Definition at line 43 of file ExpectedErrorPruneTool.cxx.

Member Function Documentation

§ CalculatePruningInfo()

TMVA::PruningInfo * TMVA::ExpectedErrorPruneTool::CalculatePruningInfo	(	DecisionTree *	dt,
		const IPruneTool::EventSample *	testEvents = `NULL`,
		Bool_t	isAutomatic = `kFALSE`
	)

virtual

Implements TMVA::IPruneTool.

Definition at line 51 of file ExpectedErrorPruneTool.cxx.

§ CountNodes()

Int_t TMVA::ExpectedErrorPruneTool::CountNodes	(	DecisionTreeNode *	node,
		Int_t	icount = `0`
	)

inlineprivate

Definition at line 86 of file ExpectedErrorPruneTool.h.

Referenced by SetPruneStrengthIncrement().

§ FindListOfNodes()

void TMVA::ExpectedErrorPruneTool::FindListOfNodes ( DecisionTreeNode * node )

private

recursive pruning of nodes using the Expected Error Pruning (EEP)

Definition at line 147 of file ExpectedErrorPruneTool.cxx.

Referenced by CalculatePruningInfo(), and SetPruneStrengthIncrement().

§ GetNodeError()

Double_t TMVA::ExpectedErrorPruneTool::GetNodeError ( DecisionTreeNode * node ) const

private

Calculate an UPPER limit on the error made by the classification done by this node.

If the S/S+B of the node is f, then according to the training sample, the error rate (fraction of misclassified events by this node) is (1-f) Now f has a statistical error according to the binomial distribution hence the error on f can be estimated (same error as the binomial error for efficency calculations ( sigma = sqrt(eff(1-eff)/nEvts ) )

Definition at line 190 of file ExpectedErrorPruneTool.cxx.

Referenced by FindListOfNodes(), GetSubTreeError(), and SetPruneStrengthIncrement().