Minor class in multi-class classification with too few samples to stratify

[pplonski]

In the case of too few samples to perform stratification there is an error thrown:

The least populated class in y has only 2 members, which is less than n_splits=5.

Maybe we can detect such situations and upsample minor classes? For sure it is related to #157 However, this issue requires rather a quick fix and #157 requires a larger treatment of unbalanced datasets.

[shahules786]

Should auto ml do upsampling w/o the user concern? Anyway, the model isn't going to learn anything for that class. Isn't it better to inform the user through a warning that stratification is not possible? @pplonski

[pplonski]

I've added a function _handle_drastic_imbalance() that assures that there is always at least 20 (or k_folds if set) samples per class.