In a multilabel setting a training example could be a, b, (a, b), d, c, (d, c), etc. This makes it a bit hard to come up with a helpful confusion matrix because the number of columns or rows could be very large - as I understand it, you wouldn't have a column or row for just a or b, but also (a, b).

What do people usually do in these cases? Do they usually create a column or row for every possible combination, or do they simplify somehow?

In case you'd like to provide code I am using Weka, but my question is primarily about the best practice.

  • $\begingroup$ Hi, did you find any suitable vizualisation for your multilabel classification? I'm currently using bar charts where each class has a bar for missed and one for false predictions. But that's not as nice as a confusion matrix. $\endgroup$
    – raspi
    Oct 2, 2018 at 9:48


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