How to handle highly Imabalanced classification?
have you already tried using only very little of the -ve cases? So for example to train your model on 900 points total, 600/300? Then stratified sampling should still work fine. Then I'd evaluate your model based on it's ability to predict -ve cases and just monitor the performance it get's on the (in your case) gigantic test dataset that the model hasn't seen before.
Also, you could generate &quot;synthetic&quot; +ve cases by bootstrapping from the real data to see how that influences the model. Similar to what is described here: https://sci2s.ugr.es/keel/pdf/specific/articulo/dupret_science.pdf