You've shipped a machine learning model that looked acceptable during development, but it is now underperforming relative to expectations. The team wants a structured way to diagnose whether the issue comes from data quality, evaluation setup, model fit, or decision threshold choices.
If a machine learning model is underperforming, what steps would you take to diagnose and improve it?
