Which sampling method is best for very unbalanced data?

[viandres]

Hi!

I am wondering, which of the implemented sampling strategies handles unbalanced data best?
I believe if I get the top 10000 uncertain data instances, but 99 % are in the same class, this would not help much for the next training process iteration, right?

Thank you in advance!

[TomKingsfordUoA]

For unbalanced data, where the estimator hasn't been trained on minority classes, typically the uncertainty measure fails to give epistemic uncertainty so won't (necessarily) sample the minority classes. Unlike uncertainty-based active learning, diversity-based AL handles this well. I've produced some diversity-based implementations privately and will look to submit a PR in the near future.