Minimum number of rows in a class about smote_variants HOT 1 OPEN

Great to hear! :) Well, yeah, important question. Most of the techniques work with as few as 2 samples from a class. However, in some cases, the operating principles require more. For example, there are some techniques which apply k-fold cross validation to check how much the generated samples fit the original distribution. In these cases at least k rows are needed.

On the flipside, just as you mention, the sample should be somewhat representative of the population. So even though the oversamplers work with 2 samples, one cannot expect useful results. I like thinking of oversampling as a highly regularized and guided kernel density estimation based sampling, and consequently, the more samples we have, the better results can be expected. But generally, I don't know about any guidance on the lowest number of samples, except the limitations implied by the operating principles of the methods.

from smote_variants.