Hi @Danik , I have reviewed this.

1) Is there documentation for PSI and other metrics?
Public docs list PSI in the drift table and give thresholds, but don’t detail the exact algorithm.
Internally, numeric PSI uses ~1000 quantiles, equal‑height binning on the baseline, plus a tiny smoothing epsilon for empty bins.

2) Why tiny avg_delta/Wasserstein (~0.01) but PSI differs (0.02 vs ~2.2)?
Different sensitivity: small shifts crossing baseline quantile bin edges can make bin proportions change a lot, so PSI jumps while mean/Wasserstein stay small.
Metrics like F1/recall can cluster near 0 or 1, so bin‑edge effects hit harder and inflate PSI.

3) Why identical arrays give PSI ≈ 0.041 (but Wasserstein=0, avg_delta≈0)?
Quantile‑based approximation + smoothing means PSI can be slightly >0 even when others read as 0.
Double‑check same windows/counts; PSI uses per‑bin proportions and quantile arrays may differ a hair, preventing an exact zero.

Ref Docs - https://docs.databricks.com/aws/en/data-quality-monitoring/data-profiling/monitor-output
One good external doc about PSI - https://medium.com/model-monitoring-psi/population-stability-index-psi-ab133b0a5d42