I have a harsh case study (in my mind).
The problem is I need make binary classification on Quality of Service (good or bad). I have a feedback on quality on groups of devices belonging to company. I have some good information on each device (same features for all devices). However, the number of devices can change from a company to another.
I would like to classify the Quality thanks to each devices information.
We usually aggregate the data to each company, but I want to try a new approach in order to be able to identify the device(s) in default.
I think to use ensemble method (calculating a score for each device and finally calculating global score for quality) or interaction_cst (HistGradientBoostingClassifier of Sklearn for instance). Nevertheless, the fact that the number of devices can vary is an obstacle for me. Also, because there are the same features for each devices, I think that using the same model to score if preferable but I haven't the quality of the device itself.
So, I you have advice on these problematics, I'll be glad to exchange with you :)