Imbalanced learn: regrets and onwards

Imbalanced learn is one of the most popular scikit-learn projects out there. It has support for resampling techniques which historically have always been used for imbalanced classification use-cases. However, now that we are a few years down the line, it may be time to start rethinking the library. As it turns out, other techniques may be preferable. We talk to the maintainer, Guillaume Lemaitre, to discuss the lessons that have been learned over the last decade.

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