Start by framing the problem and defining success metrics, then walk through a structured pipeline: quick EDA to understand data quality and distributions, feature engineering and selection, model selection with rationale, training with validation, and evaluation using appropriate metrics. Emphasize trade-offs and decisions at each step, and mention how you would productionize or iterate.
Pro tip: Always start with a baseline model (e.g., logistic regression or majority class) to set a performance floor, and use cross-validation to avoid overfitting; this shows you value robustness over flashy results.
Clarify the business objective, target variable, and success metrics. Perform light EDA: check data types, missing values, distributions, and class balance.
Handle missing values, encode categorical variables, scale numerical features, and create relevant features. Use domain knowledge to guide feature creation.
Apply filter, wrapper, or embedded methods to select important features, reducing dimensionality and improving model performance.
Choose candidate models (e.g., logistic regression, random forest, gradient boosting) based on data size, interpretability, and performance. Train with cross-validation and hyperparameter tuning.
Evaluate using appropriate metrics (e.g., precision, recall, F1, AUC-ROC) and confusion matrix. Analyze errors, iterate on features or models, and consider deployment constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Justified logistic regression as a baseline.
Frame your answer as a structured trade-off analysis: start by stating the business problem and constraints, then compare the chosen model against alternatives on specific axes like performance, latency, cost, and maintainability. Conclude by explaining how you validated the choice and what you would do if conditions changed.
Pro tip: Quantify trade-offs with real numbers from your experience (e.g., 'XGBoost gave 2% lower AUC but 10x faster inference than the neural net') and acknowledge that the 'best' model depends on context—this shows you optimize for business impact, not just metrics.
Briefly describe the business objective (e.g., fraud detection, price prediction) and key constraints such as latency, throughput, interpretability, and budget. This sets the stage for why certain models were even considered.
Name 2-3 other models you evaluated (e.g., logistic regression, random forest, transformer) and why they were plausible candidates. This shows you did a thorough search, not just picked a favorite.
Systematically compare the models on performance metrics (AUC, F1), inference speed, training cost, scalability, and maintainability. Use concrete numbers or relative comparisons to make the trade-offs tangible.
State why the chosen model won (e.g., best balance of accuracy and latency) and how you validated it (offline metrics, A/B test, shadow deployment). Mention any risks and mitigation.
Discuss what you learned and how you would revisit the choice if data volume, latency requirements, or business goals changed. This highlights your ability to handle ambiguity and evolving constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that accuracy can be misleading with imbalanced classes, then quantify the imbalance and discuss alternative metrics like precision, recall, F1, and AUC-ROC. Finally, tie the choice of metric to the business objective, such as minimizing false positives in fraud detection or maximizing recall for user safety.
Pro tip: At Coinbase, where fraud and security are critical, emphasize metrics that align with the cost of false positives vs. false negatives, and mention how you'd monitor the chosen metric in production.
Calculate the ratio of positive to negative classes and state whether it's imbalanced (e.g., 1:100). Explain how accuracy would be misleading, such as a naive model predicting the majority class achieving high accuracy.
Discuss the relative costs of false positives and false negatives in the context of Coinbase's products (e.g., fraud detection, transaction monitoring). This determines whether precision or recall is more important.
Propose metrics like precision, recall, F1-score, AUC-ROC, or AUC-PR, and justify why they are better suited for imbalanced data. Mention that AUC-PR is often preferred when the positive class is rare.
Explain that the decision threshold can be tuned to balance precision and recall based on business needs, and that probability calibration may be necessary for some metrics.
Emphasize using stratified cross-validation to ensure each fold preserves the class distribution, and to get reliable estimates of the chosen metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.