```// @(#)root/tmva \$Id\$
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss

/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
* Package: TMVA                                                                  *
* Class  : RegressionVariance                                                    *
* Web    : http://tmva.sourceforge.net                                           *
*                                                                                *
* Description: Calculate the separation critiera useded in regression            *
*                                                                                *
*          There are two things: the Separation Index, and the Separation Gain   *
*          Separation Index:                                                     *
*          Measure of the "Variance" of a sample.                                *
*                                                                                *
*          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  *
*                                                                                *
*                                                                                *
* Authors (alphabetical):                                                        *
*      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
*                                                                                *
*      CERN, Switzerland                                                         *
*      U. of Victoria, Canada                                                    *
*      Heidelberg U., Germany                                                    *
*                                                                                *
* Redistribution and use in source and binary forms, with or without             *
* modification, are permitted according to the terms listed in LICENSE           *
**********************************************************************************/

#ifndef ROOT_TMVA_RegressionVariance
#define ROOT_TMVA_RegressionVariance

//////////////////////////////////////////////////////////////////////////
//                                                                      //
// RegressionVariance                                                   //
//                                                                      //
// Calculate the "SeparationGain" for Regression analysis               //
// separation critiera used in various training algorithms              //
//                                                                      //
// There are two things: the Separation Index, and the Separation Gain  //
// Separation Index:                                                    //
// Measure of the "Variance" of a sample.                               //
//                                                                      //
// 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 //
//                                                                      //
//////////////////////////////////////////////////////////////////////////

#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif

#ifndef ROOT_TString
#include "TString.h"
#endif

namespace TMVA {

class RegressionVariance {

public:

//default constructor
RegressionVariance(){fName = "Variance for Regression";}

//copy constructor
RegressionVariance( const RegressionVariance& s ): fName ( s.fName ) {}

// destructor
virtual ~RegressionVariance(){}

// Return the gain in separation of the original sample is splitted in two sub-samples
// (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right)
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 );

// Return the separation index (a measure for "purity" of the sample")
virtual Double_t GetSeparationIndex( const Double_t &n, const Double_t &target, const Double_t &target2 );

// Return the name of the concrete Index implementation
TString GetName() { return fName; }

protected:

TString fName;  // name of the concrete Separation Index impementation

ClassDef(RegressionVariance,0) // Interface to different separation critiera used in training algorithms
};

} // namespace TMVA

#endif
```
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