How to predict multiple regressions from a model?

(WG) #1

I’m not sure if I’m phrasing this right, but what I’m attempting to do is to predict five values from a single model that are continuous (e.g., predict the number of assists, blocks, points, rebounds, and steals for a given basketball player).

I’m thinking that the right approach would be to treat this like a multi-label classification problem except that instead of using binary cross entropy, I’d just use MSE as the loss function.

If anyone has done this I’d love to know if my approach makes sense, and if not, what would.

Prediction of a scalar with a CNN
(Felipe Paula) #2

Maybe a starting approach would be a network for each variable. You would train a model for number of assists, a model for number of blocks and so on. Using a plain MSE as loss function in a “multi-regression” layer could be problematic since the variables are on different scales. Do you have many attributes to predict?

How we do regression when output in a matrix?
(WG) #3

Yes, 50+ attributes and perhaps more after feature engineering

(Dilip Thiagarajan) #4

I don’t know if this is the best idea for this context - it’d be ideal to have the weights be shared across the networks, i.e. use the same network for predicting the outputs.

(Lankinen) #5

I agree. I have this same problem and if I come up with somethig I will tell you.