So im working with more than 100 thousand samples dota2 dataset which consist of the winner and the "hero" composition from each match. I was trying to build winner of the match prediction model similiar to this [http://jmcauley.ucsd.edu/cse255/projects/fa15/018.pdf]. so the vector input is
Xi= 1 if hero i on radiant side, 0 otherwise.
X(119+i) = 1 if hero i on dire side, 0 otherwise
The vector X consist 238 entri since there are 119 kind of heroes. Each vector represent the composition of the heroes that is played within each match. Each match always consist of exactly 10 heroes (5 radiant side 5 dire side).
From this set up i would have a binary matrix of 100k times (222 + 1) dimension with row represent how many samples and columns represent features, +1 columns for the label vector (0 and 1, 1 meaning radiant side win)
so if i dot product between two column vector of my matrix, i can get how many times hero i played with hero j on all the samples.
so if i hadamard product between two column vector of my matrix and the result of that we dot product to the vector column label i can get how many times hero i played with hero j and win.
with this i can calculate the total weight of each entri per samples that corresponding to the vector label. i could get very high coorelation between this "new features" to the label vector. but i cant find any references to this problem in statistics textbook on binary data.