ROOT   6.14/05 Reference Guide
TMVA::RuleFitParams Class Reference

A class doing the actual fitting of a linear model using rules as base functions.

Definition at line 53 of file RuleFitParams.h.

## Public Member Functions

RuleFitParams ()
constructor More...

virtual ~RuleFitParams ()
destructor More...

Int_t FindGDTau ()
This finds the cutoff parameter tau by scanning several different paths. More...

UInt_t GetPathIdx1 () const

UInt_t GetPathIdx2 () const

UInt_t GetPerfIdx1 () const

UInt_t GetPerfIdx2 () const

void Init ()
Initializes all parameters using the RuleEnsemble and the training tree. More...

void InitGD ()
Initialize GD path search. More...

Double_t LossFunction (const Event &e) const
Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

Double_t LossFunction (UInt_t evtidx) const
Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

Double_t LossFunction (UInt_t evtidx, UInt_t itau) const
Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

void MakeGDPath ()
The following finds the gradient directed path in parameter space. More...

Double_t Penalty () const
This is the "lasso" penalty To be used for regression. More...

Double_t Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const
risk assessment More...

Double_t Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const
risk assessment for tau model <itau> More...

Double_t RiskPath () const

Double_t RiskPerf () const

Double_t RiskPerf (UInt_t itau) const

UInt_t RiskPerfTst ()
Estimates the error rate with the current set of parameters. More...

void SetGDErrScale (Double_t s)

void SetGDNPathSteps (Int_t np)

void SetGDPathStep (Double_t s)

void SetGDTau (Double_t t)

void SetGDTauPrec (Double_t p)

void SetGDTauRange (Double_t t0, Double_t t1)

void SetGDTauScan (UInt_t n)

void SetMsgType (EMsgType t)

void SetRuleFit (RuleFit *rf)

Int_t Type (const Event *e) const

## Protected Types

typedef std::vector< const TMVA::Event * >::const_iterator EventItr

## Protected Member Functions

Double_t CalcAverageResponse ()
calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() ! More...

Double_t CalcAverageResponseOLD ()

Double_t CalcAverageTruth ()
calculate the average truth More...

void CalcFStar ()
Estimates F* (optimum scoring function) for all events for the given sets. More...

void CalcGDNTau ()

void CalcTstAverageResponse ()
calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used More...

Double_t ErrorRateBin ()
Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F*(x)) (y-F*(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —. More...

Double_t ErrorRateReg ()
Estimates the error rate with the current set of parameters This code is pretty messy at the moment. More...

Double_t ErrorRateRoc ()
Estimates the error rate with the current set of parameters. More...

Double_t ErrorRateRocRaw (std::vector< Double_t > &sFsig, std::vector< Double_t > &sFbkg)
Estimates the error rate with the current set of parameters. More...

void ErrorRateRocTst ()
Estimates the error rate with the current set of parameters. More...

void EvaluateAverage (UInt_t ind1, UInt_t ind2, std::vector< Double_t > &avsel, std::vector< Double_t > &avrul)
evaluate the average of each variable and f(x) in the given range More...

void EvaluateAveragePath ()

void EvaluateAveragePerf ()

void FillCoefficients ()
helper function to store the rule coefficients in local arrays More...

void InitNtuple ()
initializes the ntuple More...

make test gradient vector for all tau same algorithm as MakeGradientVector() More...

Double_t Optimism ()
implementation of eq. More...

void UpdateCoefficients ()
Establish maximum gradient for rules, linear terms and the offset. More...

void UpdateTstCoefficients ()
Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range! More...

## Protected Attributes

std::vector< Double_tfAverageRulePath

std::vector< Double_tfAverageRulePerf

std::vector< Double_tfAverageSelectorPath

std::vector< Double_tfAverageSelectorPerf

Double_t fAverageTruth

Double_t fbkgave

Double_t fbkgrms

std::vector< Double_tfFstar

Double_t fFstarMedian

std::vector< std::vector< Double_t > > fGDCoefLinTst

std::vector< std::vector< Double_t > > fGDCoefTst

Double_t fGDErrScale

std::vector< Double_tfGDErrTst

std::vector< Char_tfGDErrTstOK

Int_t fGDNPathSteps

UInt_t fGDNTau

UInt_t fGDNTauTstOK

TTreefGDNtuple

std::vector< Double_tfGDOfsTst

Double_t fGDPathStep

Double_t fGDTau

Double_t fGDTauMax

Double_t fGDTauMin

Double_t fGDTauPrec

UInt_t fGDTauScan

std::vector< Double_tfGDTauVec

std::vector< std::vector< Double_t > > fGradVecLinTst

std::vector< std::vector< Double_t > > fGradVecTst

Double_t fNEveEffPath

Double_t fNEveEffPerf

UInt_t fNLinear

UInt_t fNRules

Double_tfNTCoeff

Double_t fNTErrorRate

Double_tfNTLinCoeff

Double_t fNTNuval

Double_t fNTOffset

Double_t fNTRisk

UInt_t fPathIdx1

UInt_t fPathIdx2

UInt_t fPerfIdx1

UInt_t fPerfIdx2

RuleEnsemblefRuleEnsemble

RuleFitfRuleFit

Double_t fsigave

Double_t fsigrms

## Private Member Functions

MsgLoggerLog () const
message logger More...

## Private Attributes

MsgLoggerfLogger

#include <TMVA/RuleFitParams.h>

## ◆ EventItr

 typedef std::vector::const_iterator TMVA::RuleFitParams::EventItr
protected

Definition at line 134 of file RuleFitParams.h.

## ◆ RuleFitParams()

 TMVA::RuleFitParams::RuleFitParams ( )

constructor

Definition at line 65 of file RuleFitParams.cxx.

## ◆ ~RuleFitParams()

 TMVA::RuleFitParams::~RuleFitParams ( )
virtual

destructor

Definition at line 105 of file RuleFitParams.cxx.

## ◆ CalcAverageResponse()

 Double_t TMVA::RuleFitParams::CalcAverageResponse ( )
protected

calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() !

note that 0 offset is used

Definition at line 1519 of file RuleFitParams.cxx.

## ◆ CalcAverageResponseOLD()

 Double_t TMVA::RuleFitParams::CalcAverageResponseOLD ( )
protected

## ◆ CalcAverageTruth()

 Double_t TMVA::RuleFitParams::CalcAverageTruth ( )
protected

calculate the average truth

Definition at line 1534 of file RuleFitParams.cxx.

## ◆ CalcFStar()

 void TMVA::RuleFitParams::CalcFStar ( )
protected

Estimates F* (optimum scoring function) for all events for the given sets.

The result is used in ErrorRateReg(). — NOT USED —

Definition at line 886 of file RuleFitParams.cxx.

## ◆ CalcGDNTau()

 void TMVA::RuleFitParams::CalcGDNTau ( )
inlineprotected

Definition at line 140 of file RuleFitParams.h.

## ◆ CalcTstAverageResponse()

 void TMVA::RuleFitParams::CalcTstAverageResponse ( )
protected

calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used

Definition at line 1498 of file RuleFitParams.cxx.

## ◆ ErrorRateBin()

 Double_t TMVA::RuleFitParams::ErrorRateBin ( )
protected

Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F*(x)) (y-F*(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —.

Definition at line 1011 of file RuleFitParams.cxx.

## ◆ ErrorRateReg()

 Double_t TMVA::RuleFitParams::ErrorRateReg ( )
protected

Estimates the error rate with the current set of parameters This code is pretty messy at the moment.

Cleanup is needed. – NOT USED —

Definition at line 967 of file RuleFitParams.cxx.

## ◆ ErrorRateRoc()

 Double_t TMVA::RuleFitParams::ErrorRateRoc ( )
protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area. This works but is less efficient than calculating the Risk using RiskPerf().

Definition at line 1112 of file RuleFitParams.cxx.

## ◆ ErrorRateRocRaw()

 Double_t TMVA::RuleFitParams::ErrorRateRocRaw ( std::vector< Double_t > & sFsig, std::vector< Double_t > & sFbkg )
protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

Definition at line 1045 of file RuleFitParams.cxx.

## ◆ ErrorRateRocTst()

 void TMVA::RuleFitParams::ErrorRateRocTst ( )
protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

See comment under ErrorRateRoc().

Definition at line 1160 of file RuleFitParams.cxx.

## ◆ EvaluateAverage()

 void TMVA::RuleFitParams::EvaluateAverage ( UInt_t ind1, UInt_t ind2, std::vector< Double_t > & avsel, std::vector< Double_t > & avrul )
protected

evaluate the average of each variable and f(x) in the given range

Definition at line 209 of file RuleFitParams.cxx.

## ◆ EvaluateAveragePath()

 void TMVA::RuleFitParams::EvaluateAveragePath ( )
inlineprotected

Definition at line 181 of file RuleFitParams.h.

## ◆ EvaluateAveragePerf()

 void TMVA::RuleFitParams::EvaluateAveragePerf ( )
inlineprotected

Definition at line 184 of file RuleFitParams.h.

## ◆ FillCoefficients()

 void TMVA::RuleFitParams::FillCoefficients ( )
protected

helper function to store the rule coefficients in local arrays

Definition at line 869 of file RuleFitParams.cxx.

## ◆ FindGDTau()

 Int_t TMVA::RuleFitParams::FindGDTau ( )

This finds the cutoff parameter tau by scanning several different paths.

Definition at line 450 of file RuleFitParams.cxx.

## ◆ GetPathIdx1()

 UInt_t TMVA::RuleFitParams::GetPathIdx1 ( ) const
inline

Definition at line 95 of file RuleFitParams.h.

## ◆ GetPathIdx2()

 UInt_t TMVA::RuleFitParams::GetPathIdx2 ( ) const
inline

Definition at line 96 of file RuleFitParams.h.

## ◆ GetPerfIdx1()

 UInt_t TMVA::RuleFitParams::GetPerfIdx1 ( ) const
inline

Definition at line 97 of file RuleFitParams.h.

## ◆ GetPerfIdx2()

 UInt_t TMVA::RuleFitParams::GetPerfIdx2 ( ) const
inline

Definition at line 98 of file RuleFitParams.h.

## ◆ Init()

 void TMVA::RuleFitParams::Init ( void )

Initializes all parameters using the RuleEnsemble and the training tree.

Definition at line 115 of file RuleFitParams.cxx.

## ◆ InitGD()

 void TMVA::RuleFitParams::InitGD ( )

Initialize GD path search.

Definition at line 374 of file RuleFitParams.cxx.

## ◆ InitNtuple()

 void TMVA::RuleFitParams::InitNtuple ( )
protected

initializes the ntuple

Definition at line 186 of file RuleFitParams.cxx.

## ◆ Log()

 MsgLogger& TMVA::RuleFitParams::Log ( ) const
inlineprivate

message logger

Definition at line 258 of file RuleFitParams.h.

## ◆ LossFunction() [1/3]

 Double_t TMVA::RuleFitParams::LossFunction ( const Event & e ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 279 of file RuleFitParams.cxx.

## ◆ LossFunction() [2/3]

 Double_t TMVA::RuleFitParams::LossFunction ( UInt_t evtidx ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 291 of file RuleFitParams.cxx.

## ◆ LossFunction() [3/3]

 Double_t TMVA::RuleFitParams::LossFunction ( UInt_t evtidx, UInt_t itau ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 303 of file RuleFitParams.cxx.

## ◆ MakeGDPath()

 void TMVA::RuleFitParams::MakeGDPath ( )

The following finds the gradient directed path in parameter space.

More work is needed... FT, 24/9/2006

The algorithm is currently as follows (if not otherwise stated, the sample used below is [fPathIdx1,fPathIdx2]):

1. Set offset to -average(y(true)) and all coefs=0 => average of F(x)==0
2. FindGDTau() : start scanning using several paths defined by different tau choose the tau yielding the best path
3. start the scanning the chosen path
4. check error rate at a given frequency data used for check: [fPerfIdx1,fPerfIdx2]
5. stop when either of the following conditions are fullfilled:
1. loop index==fGDNPathSteps
2. error > fGDErrScale*errmin
3. only in DEBUG mode: risk is not monotonously decreasing

The algorithm will warn if:

1. the error rate was still decreasing when loop finished -> increase fGDNPathSteps!
2. minimum was found at an early stage -> decrease fGDPathStep
3. DEBUG: risk > previous risk -> entered chaotic region (regularization is too small)

Definition at line 539 of file RuleFitParams.cxx.

protected

Definition at line 1382 of file RuleFitParams.cxx.

protected

Definition at line 1264 of file RuleFitParams.cxx.

## ◆ Optimism()

 Double_t TMVA::RuleFitParams::Optimism ( )
protected

implementation of eq.

7.17 in Hastie,Tibshirani & Friedman book this is the covariance between the estimated response yhat and the true value y. NOT REALLY SURE IF THIS IS CORRECT! — THIS IS NOT USED —

Definition at line 926 of file RuleFitParams.cxx.

## ◆ Penalty()

 Double_t TMVA::RuleFitParams::Penalty ( ) const

This is the "lasso" penalty To be used for regression.

— NOT USED —

Definition at line 357 of file RuleFitParams.cxx.

## ◆ Risk() [1/2]

 Double_t TMVA::RuleFitParams::Risk ( UInt_t ind1, UInt_t ind2, Double_t neff ) const

risk assessment

Definition at line 315 of file RuleFitParams.cxx.

## ◆ Risk() [2/2]

 Double_t TMVA::RuleFitParams::Risk ( UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau ) const

risk assessment for tau model <itau>

Definition at line 335 of file RuleFitParams.cxx.

## ◆ RiskPath()

 Double_t TMVA::RuleFitParams::RiskPath ( ) const
inline

Definition at line 112 of file RuleFitParams.h.

## ◆ RiskPerf() [1/2]

 Double_t TMVA::RuleFitParams::RiskPerf ( ) const
inline

Definition at line 113 of file RuleFitParams.h.

## ◆ RiskPerf() [2/2]

 Double_t TMVA::RuleFitParams::RiskPerf ( UInt_t itau ) const
inline

Definition at line 114 of file RuleFitParams.h.

## ◆ RiskPerfTst()

 UInt_t TMVA::RuleFitParams::RiskPerfTst ( )

Estimates the error rate with the current set of parameters.

using the <Perf> subsample. Return the tau index giving the lowest error

Definition at line 1206 of file RuleFitParams.cxx.

## ◆ SetGDErrScale()

 void TMVA::RuleFitParams::SetGDErrScale ( Double_t s )
inline

Definition at line 89 of file RuleFitParams.h.

## ◆ SetGDNPathSteps()

 void TMVA::RuleFitParams::SetGDNPathSteps ( Int_t np )
inline

Definition at line 69 of file RuleFitParams.h.

## ◆ SetGDPathStep()

 void TMVA::RuleFitParams::SetGDPathStep ( Double_t s )
inline

Definition at line 72 of file RuleFitParams.h.

## ◆ SetGDTau()

 void TMVA::RuleFitParams::SetGDTau ( Double_t t )
inline

Definition at line 86 of file RuleFitParams.h.

## ◆ SetGDTauPrec()

 void TMVA::RuleFitParams::SetGDTauPrec ( Double_t p )
inline

Definition at line 90 of file RuleFitParams.h.

## ◆ SetGDTauRange()

 void TMVA::RuleFitParams::SetGDTauRange ( Double_t t0, Double_t t1 )
inline

Definition at line 75 of file RuleFitParams.h.

## ◆ SetGDTauScan()

 void TMVA::RuleFitParams::SetGDTauScan ( UInt_t n )
inline

Definition at line 83 of file RuleFitParams.h.

## ◆ SetMsgType()

 void TMVA::RuleFitParams::SetMsgType ( EMsgType t )

Definition at line 1563 of file RuleFitParams.cxx.

## ◆ SetRuleFit()

 void TMVA::RuleFitParams::SetRuleFit ( RuleFit * rf )
inline

Definition at line 66 of file RuleFitParams.h.

## ◆ Type()

 Int_t TMVA::RuleFitParams::Type ( const Event * e ) const

Definition at line 1557 of file RuleFitParams.cxx.

## ◆ UpdateCoefficients()

 void TMVA::RuleFitParams::UpdateCoefficients ( )
protected

Establish maximum gradient for rules, linear terms and the offset.

Definition at line 1448 of file RuleFitParams.cxx.

## ◆ UpdateTstCoefficients()

 void TMVA::RuleFitParams::UpdateTstCoefficients ( )
protected

Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range!

Definition at line 1334 of file RuleFitParams.cxx.

## ◆ fAverageRulePath

 std::vector TMVA::RuleFitParams::fAverageRulePath
protected

Definition at line 209 of file RuleFitParams.h.

## ◆ fAverageRulePerf

 std::vector TMVA::RuleFitParams::fAverageRulePerf
protected

Definition at line 211 of file RuleFitParams.h.

## ◆ fAverageSelectorPath

 std::vector TMVA::RuleFitParams::fAverageSelectorPath
protected

Definition at line 208 of file RuleFitParams.h.

## ◆ fAverageSelectorPerf

 std::vector TMVA::RuleFitParams::fAverageSelectorPerf
protected

Definition at line 210 of file RuleFitParams.h.

## ◆ fAverageTruth

 Double_t TMVA::RuleFitParams::fAverageTruth
protected

Definition at line 236 of file RuleFitParams.h.

## ◆ fbkgave

 Double_t TMVA::RuleFitParams::fbkgave
protected

Definition at line 252 of file RuleFitParams.h.

## ◆ fbkgrms

 Double_t TMVA::RuleFitParams::fbkgrms
protected

Definition at line 253 of file RuleFitParams.h.

## ◆ fFstar

 std::vector TMVA::RuleFitParams::fFstar
protected

Definition at line 238 of file RuleFitParams.h.

## ◆ fFstarMedian

 Double_t TMVA::RuleFitParams::fFstarMedian
protected

Definition at line 239 of file RuleFitParams.h.

## ◆ fGDCoefLinTst

 std::vector< std::vector > TMVA::RuleFitParams::fGDCoefLinTst
protected

Definition at line 222 of file RuleFitParams.h.

## ◆ fGDCoefTst

 std::vector< std::vector > TMVA::RuleFitParams::fGDCoefTst
protected

Definition at line 221 of file RuleFitParams.h.

## ◆ fGDErrScale

 Double_t TMVA::RuleFitParams::fGDErrScale
protected

Definition at line 234 of file RuleFitParams.h.

## ◆ fGDErrTst

 std::vector TMVA::RuleFitParams::fGDErrTst
protected

Definition at line 219 of file RuleFitParams.h.

## ◆ fGDErrTstOK

 std::vector TMVA::RuleFitParams::fGDErrTstOK
protected

Definition at line 220 of file RuleFitParams.h.

## ◆ fGDNPathSteps

 Int_t TMVA::RuleFitParams::fGDNPathSteps
protected

Definition at line 233 of file RuleFitParams.h.

## ◆ fGDNTau

 UInt_t TMVA::RuleFitParams::fGDNTau
protected

Definition at line 226 of file RuleFitParams.h.

## ◆ fGDNTauTstOK

 UInt_t TMVA::RuleFitParams::fGDNTauTstOK
protected

Definition at line 225 of file RuleFitParams.h.

## ◆ fGDNtuple

 TTree* TMVA::RuleFitParams::fGDNtuple
protected

Definition at line 241 of file RuleFitParams.h.

## ◆ fGDOfsTst

 std::vector TMVA::RuleFitParams::fGDOfsTst
protected

Definition at line 223 of file RuleFitParams.h.

## ◆ fGDPathStep

 Double_t TMVA::RuleFitParams::fGDPathStep
protected

Definition at line 232 of file RuleFitParams.h.

## ◆ fGDTau

 Double_t TMVA::RuleFitParams::fGDTau
protected

Definition at line 231 of file RuleFitParams.h.

## ◆ fGDTauMax

 Double_t TMVA::RuleFitParams::fGDTauMax
protected

Definition at line 230 of file RuleFitParams.h.

## ◆ fGDTauMin

 Double_t TMVA::RuleFitParams::fGDTauMin
protected

Definition at line 229 of file RuleFitParams.h.

## ◆ fGDTauPrec

 Double_t TMVA::RuleFitParams::fGDTauPrec
protected

Definition at line 227 of file RuleFitParams.h.

## ◆ fGDTauScan

 UInt_t TMVA::RuleFitParams::fGDTauScan
protected

Definition at line 228 of file RuleFitParams.h.

## ◆ fGDTauVec

 std::vector< Double_t > TMVA::RuleFitParams::fGDTauVec
protected

Definition at line 224 of file RuleFitParams.h.

protected

Definition at line 213 of file RuleFitParams.h.

protected

Definition at line 214 of file RuleFitParams.h.

protected

Definition at line 217 of file RuleFitParams.h.

protected

Definition at line 216 of file RuleFitParams.h.

## ◆ fLogger

 MsgLogger* TMVA::RuleFitParams::fLogger
mutableprivate

Definition at line 257 of file RuleFitParams.h.

## ◆ fNEveEffPath

 Double_t TMVA::RuleFitParams::fNEveEffPath
protected

Definition at line 205 of file RuleFitParams.h.

## ◆ fNEveEffPerf

 Double_t TMVA::RuleFitParams::fNEveEffPerf
protected

Definition at line 206 of file RuleFitParams.h.

## ◆ fNLinear

 UInt_t TMVA::RuleFitParams::fNLinear
protected

Definition at line 196 of file RuleFitParams.h.

## ◆ fNRules

 UInt_t TMVA::RuleFitParams::fNRules
protected

Definition at line 195 of file RuleFitParams.h.

## ◆ fNTCoeff

 Double_t* TMVA::RuleFitParams::fNTCoeff
protected

Definition at line 247 of file RuleFitParams.h.

protected

Definition at line 245 of file RuleFitParams.h.

## ◆ fNTErrorRate

 Double_t TMVA::RuleFitParams::fNTErrorRate
protected

Definition at line 243 of file RuleFitParams.h.

## ◆ fNTLinCoeff

 Double_t* TMVA::RuleFitParams::fNTLinCoeff
protected

Definition at line 248 of file RuleFitParams.h.

## ◆ fNTNuval

 Double_t TMVA::RuleFitParams::fNTNuval
protected

Definition at line 244 of file RuleFitParams.h.

## ◆ fNTOffset

 Double_t TMVA::RuleFitParams::fNTOffset
protected

Definition at line 246 of file RuleFitParams.h.

## ◆ fNTRisk

 Double_t TMVA::RuleFitParams::fNTRisk
protected

Definition at line 242 of file RuleFitParams.h.

## ◆ fPathIdx1

 UInt_t TMVA::RuleFitParams::fPathIdx1
protected

Definition at line 201 of file RuleFitParams.h.

## ◆ fPathIdx2

 UInt_t TMVA::RuleFitParams::fPathIdx2
protected

Definition at line 202 of file RuleFitParams.h.

## ◆ fPerfIdx1

 UInt_t TMVA::RuleFitParams::fPerfIdx1
protected

Definition at line 203 of file RuleFitParams.h.

## ◆ fPerfIdx2

 UInt_t TMVA::RuleFitParams::fPerfIdx2
protected

Definition at line 204 of file RuleFitParams.h.

## ◆ fRuleEnsemble

 RuleEnsemble* TMVA::RuleFitParams::fRuleEnsemble
protected

Definition at line 193 of file RuleFitParams.h.

## ◆ fRuleFit

 RuleFit* TMVA::RuleFitParams::fRuleFit
protected

Definition at line 192 of file RuleFitParams.h.

## ◆ fsigave

 Double_t TMVA::RuleFitParams::fsigave
protected

Definition at line 250 of file RuleFitParams.h.

## ◆ fsigrms

 Double_t TMVA::RuleFitParams::fsigrms
protected

Definition at line 251 of file RuleFitParams.h.

Libraries for TMVA::RuleFitParams:
[legend]

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