ROOT 6.14/05 Reference Guide 
Calculate the "SeparationGain" for Regression analysis separation criteria 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 "leftnode" and a "rightnode" (N * Index_parent)  (N_left * Index_left)  (N_right * Index_right) this is then the quality criteria which is optimized for when trying to increase the information in the system (making the best selection
Definition at line 66 of file RegressionVariance.h.
Public Member Functions  
RegressionVariance ()  
RegressionVariance (const RegressionVariance &s)  
virtual  ~RegressionVariance () 
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) 
Separation Gain: the measure of how the quality of separation of the sample increases by splitting the sample e.g. More...  
virtual Double_t  GetSeparationIndex (const Double_t n, const Double_t target, const Double_t target2) 
Separation Index: a simple Variance. More...  
Protected Attributes  
TString  fName 
#include <TMVA/RegressionVariance.h>

inline 
Definition at line 71 of file RegressionVariance.h.

inline 
Definition at line 74 of file RegressionVariance.h.

inlinevirtual 
Definition at line 77 of file RegressionVariance.h.

inline 
Definition at line 88 of file RegressionVariance.h.
Double_t TMVA::RegressionVariance::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 "leftnode" and a "rightnode" (N * Index_parent)  (N_left * Index_left)  (N_right * Index_right) this is then the quality criteria which is optimized for when trying to increase the information in the system for the Regression: as the "Gain is maximised", the RMS (sqrt(variance)) which is used as a "separation" index should be as small as possible. the "figure of merit" here has to be (rms left+rmsright) or 1/rms...
Definition at line 70 of file RegressionVariance.cxx.

virtual 
Separation Index: a simple Variance.
Definition at line 89 of file RegressionVariance.cxx.

protected 
Definition at line 92 of file RegressionVariance.h.