5#ifndef ROOT_TMVA_VariableImportance
6#define ROOT_TMVA_VariableImportance
#define ClassDef(name, id)
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
1-D histogram with a float per channel (see TH1 documentation)}
Abstract base class for all high level ml algorithms, you can book ml methods like BDT,...
class to storage options for the differents methods
~VariableImportanceResult()
OptionMap & GetImportanceValues()
std::shared_ptr< TH1F > fImportanceHist
TH1F * GetImportanceHist()
VariableImportanceResult()
OptionMap fImportanceValues
std::unique_ptr< Factory > fClassifier
void EvaluateImportanceShort()
const VariableImportanceResult & GetResults() const
virtual void Evaluate()
Virtual method to be implemented with your algorithm.
void EvaluateImportanceRandom(UInt_t nseeds)
VariableImportanceResult fResults
void SetType(VIType type)
TH1F * GetImportance(const UInt_t nbits, std::vector< Float_t > &importances, std::vector< TString > &varNames)
void EvaluateImportanceAll()
create variable transformations