You must build an end‑to‑end scikit‑learn pipeline to predict churn_28d at decision time t0 using only features available at or before t0 (no leakage). Data columns: user_id (str), snapshot_date (date, the t0 date), country (200 levels), device (6 levels), is_premium (bool), sessions_7d (int), spend_28d (float), avg_session_seconds_28d (float), days_since_signup (int), churn_28d (bool target). Requirements: - Use ColumnTransformer with SimpleImputer, StandardScaler for numeric, and OneHotEncoder(handle_unknown='ignore') for categoricals. Justify your handling of high‑cardinality country (e.g., hashing or rare-category bucketing) and implement one approach. - Prevent leakage: ensure all aggregations (like sessions_7d) are computed strictly up to snapshot_date and that cross‑validation respects time using TimeSeriesSplit with a temporal gap. - Address class imbalance (e.g., class_weight='balanced' or calibrated resampling within CV) and output well‑calibrated probabilities via CalibratedClassifierCV. - Tune hyperparameters via cross‑validated search; report ROC‑AUC and Brier score on a holdout time slice. - Provide concise Python code that defines the pipeline, CV, calibration, and evaluation, sets random_state for reproducibility, and demonstrates model persistence (joblib). Briefly explain how you would monitor drift post‑deployment.

This question evaluates a data scientist's practical competency in building leak-safe, end-to-end scikit-learn pipelines, covering feature preprocessing (including high-cardinality categorical strategies), temporal leakage prevention, time-aware cross-validation, class imbalance handling, probability calibration, hyperparameter tuning, evaluation with ROC-AUC and Brier score, model persistence, and post-deployment drift monitoring. It is commonly asked in Machine Learning interviews to verify applied engineering and model validation skills for time-dependent tabular data, testing the Machine Learning domain at a practical application level rather than purely conceptual understanding.

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This is a Medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Apple.

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This question is commonly asked for Data Scientist candidates at Apple during technical interviews.

Build leak-safe sklearn model with calibration

You must build an end‑to‑end scikit‑learn pipeline to predict churn_28d at decision time t0 using only features available at or before t0 (no leakage). Data columns: user_id (str), snapshot_date (date, the t0 date), country (200 levels), device (6 levels), is_premium (bool), sessions_7d (int), spend_28d (float), avg_session_seconds_28d (float), days_since_signup (int), churn_28d (bool target). Requirements:

Use ColumnTransformer with SimpleImputer, StandardScaler for numeric, and OneHotEncoder(handle_unknown='ignore') for categoricals. Justify your handling of high‑cardinality country (e.g., hashing or rare-category bucketing) and implement one approach.
Prevent leakage: ensure all aggregations (like sessions_7d) are computed strictly up to snapshot_date and that cross‑validation respects time using TimeSeriesSplit with a temporal gap.
Address class imbalance (e.g., class_weight='balanced' or calibrated resampling within CV) and output well‑calibrated probabilities via CalibratedClassifierCV.
Tune hyperparameters via cross‑validated search; report ROC‑AUC and Brier score on a holdout time slice.
Provide concise Python code that defines the pipeline, CV, calibration, and evaluation, sets random_state for reproducibility, and demonstrates model persistence (joblib). Briefly explain how you would monitor drift post‑deployment.

Use ColumnTransformer with SimpleImputer, StandardScaler for numeric, and OneHotEncoder(handle_unknown='ignore') for categoricals. Justify your handling of high‑cardinality country (e.g., hashing or rare-category bucketing) and implement one approach.
Prevent leakage: ensure all aggregations (like sessions_7d) are computed strictly up to snapshot_date and that cross‑validation respects time using TimeSeriesSplit with a temporal gap.
Address class imbalance (e.g., class_weight='balanced' or calibrated resampling within CV) and output well‑calibrated probabilities via CalibratedClassifierCV.
Tune hyperparameters via cross‑validated search; report ROC‑AUC and Brier score on a holdout time slice.
Provide concise Python code that defines the pipeline, CV, calibration, and evaluation, sets random_state for reproducibility, and demonstrates model persistence (joblib). Briefly explain how you would monitor drift post‑deployment.

Build leak-safe sklearn model with calibration

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Build leak-safe sklearn model with calibration

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