ustainbolt t1_ixrqhoy wrote on November 25, 2022 at 8:29 PM

Could anyone help me by suggesting an architecture that might work for my problem? I've tried many (nn and otherwise) but haven't made much progress.

My data consist of groups of 10 people, each person has attached to them a number x_1 (identifying which person they are approx. ~1000 possibilities) and an integer x_2 (which can take one of ~150 different values). The group of 10 people then attempt to complete a task and if they are successful the data is labelled with 1, else it is labelled with 0.

You could think of the task as playing a football match (5v5) and x_2 as the position that they choose to play on the field.

Does this remind anyone of a particular class of problem?

I-am_Sleepy t1_iy3x7p2 wrote on November 28, 2022 at 3:52 PM

So what is your task again? If it is a regression problem i.e. given 10 people, calculate probability of label being 1. Then basic binary classifier should do the trick. If the problem is maximizing probability of label being 1, that will be closer to reinforcement learning. Which you can go a few way of here but for me, I would implement using genetic algorithm