Question - Is there any benchmarking `ClassImbalance.jl`'s ability to handle datasets of different sizes?

[00krishna]

Describe the bug

This is just a question about Classimbalance.jl's ability to handle different size datasets. I was working with the python imbalance-learn package, and it keeps crashing when I give it a dataset of more than 2-3 million rows. In the case of imbalanced data, this is to be expected since it takes so many false examples to get a positive one. I can find creative ways to "thin" the dataset, but I was just wondering if there were any tests on how the julia package handles larger datasets?

Thanks.

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Expected behavior

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Additional context

[DilumAluthge]

We currently don't have any benchmarks, but a pull request to add some benchmarks would be welcome!

I think there is room to improve the performance of this package.