Predictive Model Deep-Dive (End-to-End)

Pick one predictive model you know deeply (e.g., logistic regression, gradient-boosted trees, transformer classifier) and explain how it works end-to-end for a real problem you solved.

(a) Objective, loss, inductive biases/assumptions

• State the business objective and the model's training objective and loss function.
• Explain the model’s inductive biases/assumptions and when they are violated in practice.

(b) Features and validation

• Describe your feature engineering, including handling of categorical/high-cardinality features and time-based aggregates.
• Explain your validation strategy (i.i.d. vs. time-based splits), how you prevented leakage, and how you confirmed stationarity.

(c) Training

• Walk through hyperparameter search, regularization, early stopping, and handling class imbalance (weights, focal loss, resampling). Justify choices quantitatively.

(d) Training/inference issues (pick three)

• Detail three concrete issues you encountered (e.g., covariate shift, label noise, calibration drift, offline/online feature skew, latency/throughput limits).
• For each, explain how you detected, diagnosed, and fixed it (checks/plots/metrics).

(e) Evaluation beyond ROC/PR

• Discuss calibration, cost-sensitive metrics, business KPIs, and how you translated model lift into expected value.

(f) Fairness, privacy, and monitoring

• Describe fairness and privacy considerations.
• Outline post-deployment monitoring: drift detection thresholds, alerting, rollback criteria, and canarying.