D class imbalance: over/under sampling and class reweight If | Data Scientology
D class imbalance: over/under sampling and class reweight
If there's unbalanced datasets, what's the way to proceed?
The canonical answer seems to be over/under sampling and class reweighting (is there anything more?), but have these things really worked in practice for you?
What's the actual experience and practical suggestion? When to use one over the other?
