Start by framing the problem in terms of Amazon's marketing context, then walk through a structured pipeline: data preparation, model training with gradient-boosted trees (e.g., XGBoost/LightGBM), hyperparameter tuning via cross-validation, and evaluation using appropriate metrics. For feature selection, emphasize a combination of domain knowledge, statistical methods, and model-based importance, while addressing scalability and potential leakage.
Pro tip: Tie your choices to business impact—e.g., how the model will be used to optimize marketing spend—and mention practical constraints like training time and interpretability, which are valued at Amazon.
Clarify the prediction goal (e.g., conversion, ROI) and ensure data quality, handling missing values, and encoding categorical variables. Split data temporally to avoid leakage, especially for time-series marketing data.
Choose a GBT implementation (XGBoost, LightGBM, CatBoost) based on speed and categorical handling. Start with a baseline model, then iteratively add features and monitor performance.
Use Bayesian optimization or random search with cross-validation to tune key parameters like learning rate, max depth, subsample, and regularization. Prioritize parameters by impact and consider early stopping.
Select metrics aligned with business goals (e.g., AUC, lift, precision@k). Use holdout sets and cross-validation, and analyze feature importance and SHAP values for interpretability.
Start with domain knowledge to filter features, then use statistical tests (e.g., mutual information) and model-based importance (e.g., gain, permutation) to reduce dimensionality. Consider regularization and recursive feature elimination.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.