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Are there any general guidelines for dealing with imbalanced data through upsampling/downsampling?

This Google developer guide suggests performing downsampling with upweighting, but for the most part I've found upsampling usually works better in practice (some corroboration).

Is there any clear consensus or empirical study of what works in practice, or when to use which? Does it matter which classification algorithm you use?

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