The starting position is the following:

There are categories 1 and 2, as well as features A, B and C.

A representation would look like this:

enter image description here

What is a way to not only predict the occuring categories (output) based on given features (input), but also the amount for each category? As most of the time for my dataset, all or most categories are present for a given set of features, but their amount varies widely.

I tried to implement this using the random forest classifier from sklearn but did not succeed.

  • $\begingroup$ You could try using a separate regressor for each of the categories, so in this case you would have three models. $\endgroup$
    – Oxbowerce
    Jan 8 at 13:01

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