[D] LightGBM imbalanced class problem - pos_scale_weight < 1 significantly improves performance

I am working on an imbalanced classification problem (~0.2% positive class) using LightGBM and am noticing a significant increase in performance when I use pos_scale_weight < 1 vs pos_scale_weight > 1. I'm talking about a cross-validation PR-AUC of 0.8 vs 0.2 kind of improvement.

I am new to imbalanced class problems and this result is not very intuitive for me at all. I feel like it has something to do with overfitting.

lightgbm machinelearning performance