An interface to calculate the "SeparationGain" for different separation criteria used in various training algorithms.
There are two things: the Separation Index, and the Separation Gain Separation Index: Measure of the "purity" of a sample. If all elements (events) in the sample belong to the same class (e.g. signal or background), than the separation index is 0 (meaning 100% purity (or 0% purity as it is symmetric. The index becomes maximal, for perfectly mixed samples eg. purity=50% , N_signal = N_bkg
Separation Gain: the measure of how the quality of separation of the sample increases by splitting the sample e.g. into a "left-node" and a "right-node" (N * Index_parent) - (N_left * Index_left) - (N_right * Index_right) this is then the quality criterion which is optimized for when trying to increase the information in the system (making the best selection
Definition at line 82 of file SeparationBase.h.