## Context You built a **binary sentiment classification** model (e.g., positive vs. negative) and need to deploy it in a product where actions depend on the model’s output. ## Questions 1. **Walk through your ML pipeline** end-to-end: - Data sourcing/labeling and dataset construction (train/validation/test splits). - Feature design or model choice (e.g., TF-IDF + linear model vs. transformer). - Training procedure and evaluation setup. - Key practical challenges (class imbalance, noisy labels, distribution shift) and how you handled them. 2. **Modeling choices:** - Why did you choose method/model **X** over alternatives? - What assumptions does it make, and what trade-offs does it introduce (latency, interpretability, cost, robustness)? 3. **Iterative refinement:** - Describe how you **iteratively improved** the system (e.g., error analysis → new features/data → retrain → re-evaluate). - What were your biggest learnings and what would you do differently next time? 4. **Threshold selection for deployment:** - Your model outputs a probability score. How do you choose the **decision threshold**? - Which metrics would you consider (precision, recall, F1, ROC-AUC, PR-AUC, cost-weighted loss), and which would be **primary vs. diagnostic vs. guardrail**? - How would the answer change under: - Severe class imbalance - Different costs for false positives vs. false negatives - A fixed review/ops capacity (e.g., only 1,000 items/day can be escalated) 5. **Metric definition:** - If a stakeholder proposes defining “success” as metric **XXX**, how do you evaluate whether that definition is appropriate? - What data issues (label leakage, delayed labels, sampling bias) could make the metric misleading?