newbie here. I'm starting to work on a custom model for a very specific task, so I found no pre-trained models for this task so far.
After checking (un)supervised learning approaches I believe that regression will work the best, but could you please take a look and say if there's something better around?
The data is like that:
input1(int) input2(float) input3(int) => output(int)
The set of
[inp1, inp2, inp3] is unique and can generate only one
output. There are always two and only two such sets that generate the same output (like
[2, 5.0, 3] => 0 and
[12, -3.0, 1] =>0 too). It's always three inputs in a set (no less, no more), I see no reason to consider there's anything like a label, output varies from +100million to -100 million or even more in the future.
There will be no texts, the data is not time-correlated. The full dataset size is a few hundred million of such sets, if it matters.
So, is regression the best choice here? Any advice on this task for beginner?