The Misconception of Retraining: Why Model Refresh Isn’t Always the Fix

Machine learning practitioners often rely on model retraining as a quick fix for performance issues, but this approach can be misguided. Retraining may actually exacerbate problems by reinforcing biases, learning from temporary anomalies, or ignoring fundamental issues in data quality or feature engineering. Instead, teams should focus on diagnosing root causes, improving feature logic, and monitoring post-prediction KPIs to ensure models remain aligned with business goals.