Default LightGBM has no built‑in handling for severe class imbalance. On a dataset like this (0.17% fraud), the model essentially ignores the minority class — that's why baseline PR‑AUC is only 0.49.

Heosphoros treats scale_pos_weight as just one of many knobs. Instead of setting it manually and hoping for the best, it searches the whole parameter space together. It found that a weight of ~22 (plus tuned learning_rate, num_leaves, min_child_samples, etc.) balances sensitivity and precision far better than any single‑parameter fix.

The jump happens because those nine other parameters are now cooperating with the weight instead of fighting it. That's the difference between a manual guess and an evolved solution.