I rattled off the usual stuff: mean/median imputation, indicator columns for missingness, dropping rows if the rate is high enough.
Start by emphasizing that there is no one-size-fits-all solution; the choice depends on the nature of the missing data (MCAR, MAR, MNAR), the proportion of missingness, and the downstream model. Then walk through a systematic decision process: diagnose, choose a strategy (deletion, imputation, or model-native handling), and validate the impact on model performance.
Pro tip: Always create a missingness indicator feature when imputing, as the pattern of missingness itself can be predictive. Also, mention that you validate imputation choices via cross-validation and compare against a baseline to ensure it actually helps.
Quantify the amount and pattern of missing values per feature, and determine if missingness is random (MCAR, MAR) or informative (MNAR). Use visualizations and statistical tests to understand the mechanism.
Consider the proportion of missing data, the importance of the feature, and business constraints (e.g., real-time inference). Evaluate whether deletion is acceptable or if imputation is necessary.
Select from deletion (listwise/pairwise), simple imputation (mean/median/mode), model-based imputation (KNN, MICE), or model-native handling (XGBoost, LightGBM). Justify based on data type and model assumptions.
Apply the chosen method within a cross-validation pipeline to avoid data leakage. Compare model performance with and without imputation, and consider adding missingness indicators.
After deployment, monitor for changes in missingness patterns and retrain or adjust the imputation strategy as needed. Document the rationale for future reference.
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Start by clarifying the business problem and translating it into an ML formulation (e.g., classification, regression, ranking). Then walk through a structured evaluation of candidate algorithms based on data characteristics, constraints, and business metrics, and justify your final choice with trade-offs.
Pro tip: Always tie your algorithm choice back to the business impact and Amazon's leadership principles, such as Customer Obsession and Dive Deep. Mention that you would start with a simple baseline (e.g., logistic regression) before moving to complex models, and validate with offline metrics that correlate with online business metrics.
Clarify the problem, success metrics, and constraints (e.g., latency, interpretability, budget). Translate it into an ML task (e.g., binary classification, ranking).
Evaluate data size, quality, features, and label availability. Identify key requirements like real-time inference, explainability, or scalability.
Based on the problem type and constraints, list 2-3 suitable algorithms (e.g., logistic regression, gradient boosting, neural networks). Consider baselines and state-of-the-art.
Compare candidates on performance (offline metrics), interpretability, training/inference time, scalability, and maintenance. Consider business metrics like ROI or customer satisfaction.
Select the best algorithm with clear reasoning, and propose an experimentation plan (A/B test) to validate. Mention that you would iterate based on results.
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Honestly the contrast part tripped me up more than explaining Random Forests.
Start with a plain-language analogy for Random Forests, then contrast with linear regression on assumptions, interpretability, and performance. Use a concrete example to illustrate when each shines, and tie it back to real-world data science trade-offs.
Pro tip: Emphasize that Random Forests capture non-linear relationships and interactions without explicit feature engineering, but linear regression offers interpretable coefficients and faster inference—choose based on the problem's need for explainability versus predictive power.
Explain that a Random Forest is an ensemble of many decision trees, each trained on a random subset of data and features, and their predictions are averaged (regression) or voted (classification).
Describe linear regression as fitting a straight line (or hyperplane) that models the relationship between inputs and a continuous output by minimizing squared errors.
Highlight differences in assumptions (linearity vs. non-linearity), interpretability (coefficients vs. black-box), and handling of interactions and outliers.
Explain when to use each: linear regression for simple, interpretable models with linear relationships; Random Forests for complex, non-linear data where accuracy matters more than explainability.
Mention how these trade-offs apply to real-world scenarios like demand forecasting or customer segmentation, where both interpretability and predictive power are valued.
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Define overfitting and underfitting clearly, then explain how to detect each using learning curves and validation metrics, and finally describe practical techniques to address them. Emphasize the bias-variance tradeoff and how you would apply these concepts in a real-world Amazon data science context.
Pro tip: Mention that in practice, you often start by checking if the model is overfitting or underfitting using learning curves, and then apply regularization or more data accordingly. Also, highlight that underfitting can sometimes be addressed by increasing model complexity or feature engineering, while overfitting requires simplifying the model or adding more data.
Clearly explain overfitting (model learns noise, high variance) and underfitting (model too simple, high bias) with examples.
Describe how to detect each using learning curves, validation metrics, and comparing training vs. validation performance.
List techniques like regularization (L1/L2), dropout, early stopping, cross-validation, and gathering more data.
List techniques like increasing model complexity, adding features, reducing regularization, and training longer.
Discuss the bias-variance tradeoff and how to balance both in a real project, possibly with an example from A/B testing or experimentation.
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