Very new in ML...
Actual problem is more complex, but I'll give it shorter.
Train data is a collection of samples, describing features of some items. There are many samples for each item. So ML model is implied to predict a few target variables, but one time for each item:
It appears, that I can't find in sklearn or a similar kit a way to fit model with this type of train-input/train-output. Is there any way to do this without hand-implementing whole pipeline?
I've considered this answer: How to predict based on multiple samples?
But I'm not sure it's applicable, since there are many-many samples for each item
Edit 1: Final model should get a few new samples for completely new item and predict same target variables