Dear TMVA experts,
I started using TMVA recently and so far I am a happy costumer. My understanding is that TMVA training works with two input samples: signal and background. Is this correct?
However, I would like to know if it is possible to train TMVA with just one input sample. I mean: if it is possible to pass to TMVA a set of variables/distribution for a given type of events (let’s call it type-A events), so TMVA can build a likelihood for those type of events according to the type-A variable distributions and their correlations. Then later when calling GetMVAScore for a given test event, TMVA will return the likelihood for that event to be of type-A.
I am aware that this way can be less performant than discriminating between type-A and type-B of events (or between signal and background). But as said I am interested just to know what is the likelihood of a given event of being of type-A. I don’t want to confront two type of events.
I did a function that does something like this using just my type-A events as input sample: then using a variable and then building the likelihood distribution of the type-A events for this variable. Obviously this comes out flat. So far so good. Later I tested that likelihood with a another event sample and check if the events in the second sample resemble those of the type-A. This is quite rudimentary but it basically works, just with one input variable. I tried to use a second variable, but to make it working properly I have to consider the correlations between the two variables… which then I felt like building a poor-man version of TMVA.
Thanks for the feedback
Salva