You've shipped a model that is not meeting expectations, and the team wants a structured plan to improve it. You need to decide whether the issue comes from data, model choice, generalization, or decision threshold.
How would you optimize a model that is underperforming?
Use cross-validation to measure generalization reliablyApply hyperparameter tuning instead of ad hoc changesReason about bias versus varianceAdjust the operating threshold to match business trade-offs