I don't know much about models for tabular data.

That being said, it sounds like you want to pick/select a certain member/item from a group of 50? Instead of predicting the corresponding value, maybe you could just predict the index of that item.

Don't have much more to add. If you provide a bit more context about what you're trying to achieve someone may be able to provide more feedback. BTW this type of post is probably more suited for /r/learnmachinelearning , so try there too.

Maybe give an example? Maybe I’m just slow but I think it’s ambiguous the way it’s phrased

I have a quantity of 100 units that need to be allocated across 50 days. The data set is comprised of hundreds of these scenarios X quantity allocated across 50 days. The quantity varies but number of days remain the same. What's the best way to get an ML model to do this.

It sounds more like constrained optimization than ML but still too vague of an explanation for me to be helpful so I’m giving up. Good luck

One hack that I often use for this problem is to use any gradient based machine learning approach and just add a Lagrangian to the cost function. As the function converges gradually increase the langrangian until the value holds up to your tolerance for error. If your system is some sort of convex program or something nice, looking for a constrained program that solves your problem formulation may be a good bet.

This doesn't sound like you're trying to predict a number it sounds like you want to predict 50 numbers.

My first thought is to predict the 50 numbers simultaneously, and to apply softmax to the output (enforcing summing to 1), then scaling that so it sums to your desired number for each group.

Sounds like you want to predict 50 values, given 150 inputs. ML might work, but I doubt you'd have enough data to avoid over fitting.

It also sounds like it's not like there's a single correct numerical answer for any given day, rather, you're trying to find a decent distribution. So look first into constrained optimizations first, similar to budget allocation or task distribution optimizations.

If you just want to normalize everything why creating a custom activation function that just does that?

I'm not sure I understand what you mean?

You’re suggesting Softmax then normalize to whatever is required. Softmax takes the exponential of the value then performs the normalization. You might not want that exponential. You can create a layer that does the normality through a custom activation function you use in the last layer

Yes I was implicitly talking about the final activation function. With regards to softmax, he said in another comment "I have a quantity of 100 units that need to be allocated across 50 days.", so I took that to imply the outputs should be positive (hence the exponential is reasonable).

Ha good point