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[D] Multiple variable prediction handling

Hi everyone,

It’s easy to find documentation on single or multi classification and single variable regression, but when it comes to multiple regressions, I find it much harder.

If one wants to predict multiple variables (that we suppose not independent), like the consumption of a set of articles (given historical data etc…). So for each article we have, we want to predict its consumption.

What would be the most common way to handle this? What is the actual preferred approach ? For basic ML, I’m used to XGBoost and LightGBM but those two are not supporting multiple variable prediction yet. Regarding DL, I believe this would ask a tremendous amount of data (the more article we have).

Are there any papers talking about this subject? Or any piece of work that would help?

The training data can be seen as such (and we want to predict art1, art2 etc…) :

id datetime (uneven) art1 art2
1 05/09/2017 14:50:18 2 5
2 06/09/2017 02:23:55 1 7

submitted by /u/AbricotMozarella
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.