Transform multi-class problem to multi-label problem

I found this question but I need an answer to the other direction.

Example: Let's say we want to predict if a person with a certain profile wants to buy product A and/or B. So we have 2 binary classes A and B that don't exclude each other:

A     B
0     1
1     1
0     0
1     0
...

(We don't want to predict how likely it is for a person to buy B if the person has already bought A.) Does it - in general or under certain conditions - make sense to transform this problem into a single-class multi-label problem with 4 labels (4=#combinations of A and B)? What if the number of binary classes is larger than 2?

Topic machine-learning-model multilabel-classification multiclass-classification classification machine-learning

Category Data Science

1
answered at

It depends on the contextual link between A and B.

If they are completely different categories with no or low correlation, there shouldn't be necessary to have a single class multi label.

But if A and B are somehow connected, overall if they can represent a scale together (i.e. AB = [0 0] = 0 = "low impact" or AB = [1 1] = 3 = "high impact"), it could be meaningful to have a single class multi label. It all depends on the correlation and the business point of view.

If you can give more information about the purpose of A and B, I might be able to give more information.

About

Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.