class TMVA::RegressionVariance

Function Members (Methods)

public:

virtual	~RegressionVariance()
static TClass*	Class()
TString	GetName()
Double_t	GetSeparationGain(const Double_t& nLeft, const Double_t& targetLeft, const Double_t& target2Left, const Double_t& nTot, const Double_t& targetTot, const Double_t& target2Tot)
virtual Double_t	GetSeparationIndex(const Double_t& n, const Double_t& target, const Double_t& target2)
virtual TClass*	IsA() const
TMVA::RegressionVariance&	operator=(const TMVA::RegressionVariance&)
TMVA::RegressionVariance	RegressionVariance()
TMVA::RegressionVariance	RegressionVariance(const TMVA::RegressionVariance& s)
virtual void	ShowMembers(TMemberInspector& insp, char* parent)
virtual void	Streamer(TBuffer& b)
void	StreamerNVirtual(TBuffer& b)

Data Members

protected:

TString	fName	name of the concrete Separation Index impementation

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

Double_t GetSeparationGain(const Double_t& nLeft, const Double_t& targetLeft, const Double_t& target2Left, const Double_t& nTot, const Double_t& targetTot, const Double_t& target2Tot)

 Separation Gain:
 the measure of how the quality of separation of the sample increases
 by splitting the sample e.g. into a "left-node" and a "right-node"
 (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
 this is then the quality crition which is optimized for when trying
 to increase the information in the system (making the best selection

Double_t GetSeparationIndex(const Double_t& n, const Double_t& target, const Double_t& target2)

 Separation Index:  a simple Variance

RegressionVariance()

default constructor

{fName = "Variance for Regression";}

RegressionVariance(const TMVA::RegressionVariance& s)

copy constructor

{}

virtual ~RegressionVariance()

 destructor

{}

TString GetName()

 Return the name of the concrete Index implementation

{ return fName; }