Explain bias-variance, calibration, and model drift

You are interviewing for an applied ML role. Answer the following ML fundamentals questions **in a business-facing way** (i.e., start from a customer/business need, then map it to the ML concept and actions). ## 1) Bias–variance trade-off - Define **bias** and **variance** and explain the trade-off. - Give practical ways to **reduce bias**. - Give practical ways to **reduce variance**. ## 2) Confidence (probability) calibration - Define **confidence calibration** (what does it mean for a model to be calibrated?). - How would you **measure** calibration? - What are common **methods to improve** calibration? ## 3) Model drift - Define **model drift** (and distinguish common types of drift). - How would you **detect** drift in production? - What are typical mitigation actions once drift is detected? Assume a typical product scenario (e.g., ranking/recommendation, fraud detection, churn prediction, ads CTR) where the customer wants stable, reliable decisions and probabilities over time.