class TMVA::GiniIndexWithLaplace: public TMVA::SeparationBase

GiniIndexWithLaplace

Implementation of the GiniIndex With Laplace correction
as separation criterion

Large Gini Indices (maximum 0.5) mean , that the sample is well
mixed (same amount of signal and bkg)
bkg. Small Indices mean, well separated.
general defniniton:
Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2
Where: M is a smaple of whatever N elements (events)
that belong to K different classes
c(k) is the number of elements that belong to class k
for just Signal and Background classes this boils down to:
     the "Lapalace correction to the probability distribution would   //
turn the c(1)/N into (c(1)+1)/(N+2)
using this the simple Gini Index  for two classes
Gini(Sample) = 2s*b/(s+b)^2
turns into
GiniLaplace(Sample) = 2(s*b+s+b+1)/(s+b+2)^2