During a discussion on treating events with negative weights in ML I started wondering whether there is an intrinsic connection between event weights and the labeling.
Say if I label the signal events with negative weights as “background”, should I expect to have similar results as the ones obtained by labeling all signal events as “signal” with proper weights (both negative and positive)?
My naive thinking is that it should give me results along the same directions but I am not sure whether this is always the case. I can try things out and see what will happen but I wonder whether there are simple arguments (maybe for certain algorithms this is true but not for ours?).
Any insights or thoughts?
Thanks a lot!
Bing (A machine learning noob)