Structure your answer around the ML lifecycle: data ingestion and feature engineering with a feature store to prevent leakage, model training with time-based cross-validation and explicit cutoffs, and evaluation with cost-sensitive thresholding and calibration. Emphasize how each design choice addresses the specific challenges of imbalance, missingness, outliers, and drift, and discuss trade-offs and monitoring.
Pro tip: Quantify the business impact of your threshold choice by computing expected cost at different thresholds and showing how it aligns with the 20:1 cost ratio; also mention that you would monitor calibration and drift in production to trigger retraining.
Ingest sensor data and create features using a feature store that enforces point-in-time correctness, ensuring that only data available up to each hour is used for prediction. Handle missingness and outliers with robust imputation and clipping, and log all transformations for reproducibility.
Split data into training and validation sets using time-based cross-validation with explicit cutoff timestamps to simulate real-world deployment. Train a model (e.g., gradient boosting) that can handle class imbalance and provide probability outputs.
Calibrate predicted probabilities using Platt scaling or isotonic regression, then select a decision threshold that minimizes expected cost given FN=$10,000 and FP=$500. Evaluate using cost-sensitive metrics and compare to default 0.5 threshold.
Use SHAP to explain model predictions, but acknowledge caveats such as correlated features and extrapolation. Assess robustness to missingness, outliers, and distribution drift by simulating perturbations and monitoring drift metrics like PSI.
Deploy the model with the chosen threshold, and set up monitoring for data drift, calibration, and business cost. Plan for periodic retraining and threshold re-evaluation as conditions change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.