How do you deal with unbalanced classification problem?

Question

Anónimo · Accepted Answer

Sampling technique like oversampling and downsampling. Models that adapt to unbalanced data like Ada-boosting. Or use anomaly detection methods.

arman · Answer

The basic idea is sampling (oversampling like resampling) which is not a good option when dealing with huge imbalance.  Another option is to set weights for class labels (larger weights for classes with fewer samples). A really good, but tough and many times not feasible to implement, is to do oversampling by using generative networks (i.e. GANs).

Anónimo · Answer

over/undersampling, GANs (I believe they require lots of data), and class weights (sometimes refer to class weights)

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