Hi TMVA/BDT Experts,
I’m new to using TMVA and BDTs and I have a question about the renormalising of events.
I have read online that the BDT algorithms in TMVA renormalise the signal and background events such that the weights given to them e.g. signalWeightExpression or the global weight do not have affect. I was wondering if some could tell me why this is the case or point me towards some literature explaining why the normalisation does not matter?
The third link below states the following:
BDT doesn’t care about what kind of ‘normalisation’ you’ve chosen in the factory, it simply scales both signal and background beforehand to same effective number of events. This is, because otherwise the first boosting step would be essentially doing ‘just that’ and knowing this, I can also do it straight away.
This seems to be the answer but I don’t understand why this is the case. Can anyone point me to something explaining this statement? Naively I would assume if one of the backgrounds was quite rare that the MVA would not sacrifice as much signal in the classification as compared to a much more common background. In my analysis I produce dedicated background samples which then need to be scaled to one another. So I find it unusually that the BDT does not consider this scaling.
Any help would greatly appreciated