TMVA::ExpectedErrorPruneTool Class Reference

A helper class to prune a decision tree using the expected error (C4.5) method.

Uses an upper limit on the error made by the classification done by each node. If the \( \frac{S}{S+B} \) 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 efficiency calculations \( (\sigma = \sqrt{\frac{(eff(1-eff)}{nEvts}}) \)

This tool prunes branches from a tree if the expected error of a node is less than that of the sum of the error in its descendants.

Definition at line 55 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 ()
Double_t	GetPruneStrength () const
Bool_t	IsAutomatic () const
void	SetAutomatic ()
void	SetPruneStrength (Double_t alpha)

Private Member Functions
Int_t	CountNodes (DecisionTreeNode *node, Int_t icount=0)
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...
MsgLogger &	Log () const

Private Attributes
Double_t	fDeltaPruneStrength
MsgLogger *	fLogger
	the (optimal) prune sequence More...
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...

Additional Inherited Members
Public Types inherited from TMVA::IPruneTool
typedef std::vector< const Event * >	EventSample
Protected Attributes inherited from TMVA::IPruneTool
Double_t	B
Double_t	fPruneStrength
Double_t	S
	regularization parameter in pruning More...

#include <TMVA/ExpectedErrorPruneTool.h>

Inheritance diagram for TMVA::ExpectedErrorPruneTool:

Constructor & Destructor Documentation

ExpectedErrorPruneTool()

TMVA::ExpectedErrorPruneTool::ExpectedErrorPruneTool

(

)

Definition at line 59 of file ExpectedErrorPruneTool.cxx.

~ExpectedErrorPruneTool()

TMVA::ExpectedErrorPruneTool::~ExpectedErrorPruneTool ( )

virtual

Definition at line 68 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 76 of file ExpectedErrorPruneTool.cxx.

CountNodes()

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

inlineprivate

Definition at line 82 of file ExpectedErrorPruneTool.h.

FindListOfNodes()

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

private

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

Definition at line 172 of file ExpectedErrorPruneTool.cxx.

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 efficiency calculations \( (\sigma = \sqrt{\frac{(eff(1-eff)}{nEvts}}) \)

Definition at line 216 of file ExpectedErrorPruneTool.cxx.

GetSubTreeError()

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

private

calculate the expected statistical error on the subtree below "node" which is used in the expected error pruning

Definition at line 190 of file ExpectedErrorPruneTool.cxx.

Log()

MsgLogger& TMVA::ExpectedErrorPruneTool::Log ( ) const

inlineprivate

Definition at line 79 of file ExpectedErrorPruneTool.h.

SetPruneStrengthIncrement()

void TMVA::ExpectedErrorPruneTool::SetPruneStrengthIncrement ( Double_t  dalpha )

inline

Definition at line 66 of file ExpectedErrorPruneTool.h.

Member Data Documentation

fDeltaPruneStrength

Double_t TMVA::ExpectedErrorPruneTool::fDeltaPruneStrength

private

Definition at line 74 of file ExpectedErrorPruneTool.h.

fLogger

MsgLogger* TMVA::ExpectedErrorPruneTool::fLogger

mutableprivate

the (optimal) prune sequence

Definition at line 78 of file ExpectedErrorPruneTool.h.

fNodePurityLimit

Double_t TMVA::ExpectedErrorPruneTool::fNodePurityLimit

private

the stepsize for optimizing the pruning strength parameter

Definition at line 75 of file ExpectedErrorPruneTool.h.

fPruneSequence

std::vector<DecisionTreeNode*> TMVA::ExpectedErrorPruneTool::fPruneSequence

private

the purity limit for labelling a terminal node as signal

Definition at line 76 of file ExpectedErrorPruneTool.h.

Libraries for TMVA::ExpectedErrorPruneTool:

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