5#ifndef ROOT_TMVA_HyperParameterOptimisation
6#define ROOT_TMVA_HyperParameterOptimisation
#define ClassDef(name, id)
Abstract base class for all high level ml algorithms, you can book ml methods like BDT,...
std::vector< Double_t > fEffAreas
std::vector< Double_t > fTrainEff10s
std::vector< Double_t > GetSepValues()
std::vector< Double_t > GetEff01Values()
TMultiGraph * GetROCCurves(Bool_t fLegend=kTRUE)
std::vector< Float_t > fROCs
std::vector< Double_t > fTrainEff01s
std::vector< Double_t > fEff10s
std::vector< std::map< TString, Double_t > > fFoldParameters
std::vector< Double_t > GetTrainEff01Values()
std::vector< Double_t > GetTrainEff30Values()
std::shared_ptr< TMultiGraph > fROCCurves
std::vector< Double_t > GetEff10Values()
std::vector< Double_t > fSeps
std::vector< Float_t > GetROCValues()
std::vector< Double_t > fTrainEff30s
~HyperParameterOptimisationResult()
std::vector< Double_t > GetEff30Values()
std::vector< Double_t > GetSigValues()
std::vector< Double_t > fEff01s
std::vector< Double_t > GetEffAreaValues()
std::vector< Double_t > GetTrainEff10Values()
std::vector< Double_t > fSigs
std::vector< Double_t > fEff30s
HyperParameterOptimisationResult()
virtual void Evaluate()
Virtual method to be implemented with your algorithm.
std::unique_ptr< Factory > fClassifier
!
void SetFitter(TString fitType)
void SetNumFolds(UInt_t folds)
const HyperParameterOptimisationResult & GetResults() const
void SetFOMType(TString ftype)
~HyperParameterOptimisation()
HyperParameterOptimisationResult fResults
!
A TMultiGraph is a collection of TGraph (or derived) objects.
create variable transformations