4 #ifndef ROOT_TMVA_CrossValidation 5 #define ROOT_TMVA_CrossValidation 101 const std::vector<CrossValidationResult> &
GetResults()
const;
110 #endif // ROOT_TMVA_CrossValidation std::vector< Double_t > fSigs
Float_t GetROCAverage() const
std::map< UInt_t, Float_t > GetROCValues()
std::vector< Double_t > GetEff01Values()
A TMultiGraph is a collection of TGraph (or derived) objects.
Class to save the results of cross validation, the metric for the classification ins ROC and you can ...
std::unique_ptr< Factory > fClassifier
std::vector< Double_t > GetEff10Values()
std::vector< Double_t > GetSigValues()
std::vector< Double_t > fEff10s
std::vector< Double_t > GetEff30Values()
#define ClassDef(name, id)
std::vector< CrossValidationResult > fResults
void SetNumFolds(UInt_t i)
std::vector< Double_t > GetEffAreaValues()
Abstract base class for all high level ml algorithms, you can book ml methods like BDT...
std::vector< Double_t > fTrainEff01s
std::vector< Double_t > fTrainEff10s
std::vector< Double_t > fEff01s
std::vector< Double_t > GetTrainEff01Values()
Float_t GetROCStandardDeviation() const
const std::vector< CrossValidationResult > & GetResults() const
std::vector< Double_t > fTrainEff30s
CrossValidation(DataLoader *loader)
virtual void Evaluate()
Virtual method to be implemented with your algorithm.
Class to perform cross validation, splitting the dataloader into folds.
std::vector< Double_t > GetTrainEff30Values()
TMultiGraph * GetROCCurves(Bool_t fLegend=kTRUE)
std::vector< Double_t > fEffAreas
std::vector< Double_t > fSeps
Abstract ClassifierFactory template that handles arbitrary types.
std::map< UInt_t, Float_t > fROCs
TCanvas * Draw(const TString name="CrossValidation") const
std::vector< Double_t > fEff30s
std::vector< Double_t > GetTrainEff10Values()
std::shared_ptr< TMultiGraph > fROCCurves
std::vector< Double_t > GetSepValues()