I knew the basics but fumbled a bit explaining when it actually matters versus when it doesn't.
Start by clarifying that the decision depends on the algorithm's sensitivity to feature scale and the data's distribution. Then discuss specific scenarios where standardization or normalization is beneficial, such as distance-based algorithms, gradient descent optimization, and features with different units. Finally, mention cases where it's not needed, like tree-based models, and highlight the importance of applying the same transformation to test data.
Pro tip: Emphasize that while scaling can improve model performance, it also affects interpretability; for example, coefficients in linear models become comparable but lose their original unit meaning. Always consider the trade-off and document the scaling steps for reproducibility.
Determine if the algorithm is sensitive to feature scale, such as SVM, k-NN, neural networks, or PCA. These require scaling for optimal performance.
Check if features have different units or vastly different ranges. If so, scaling helps prevent features with larger magnitudes from dominating.
For normalization (e.g., min-max), consider if the data has bounded ranges or outliers. For standardization (e.g., z-score), consider if the data is approximately Gaussian.
Decide if scaling is acceptable given potential loss of interpretability. For models where coefficients are interpreted, scaling changes the meaning.
Ensure the same scaling parameters (mean, std or min, max) from training data are applied to validation and test data to avoid data leakage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business context and the nature of the nulls/zeros (missing vs. true zero). Then systematically evaluate imputation, transformation, or exclusion strategies based on data distribution, model requirements, and potential bias. Emphasize validation and iteration to ensure the chosen approach doesn't harm model performance or interpretability.
Pro tip: Always check if zeros are actually missing values encoded as zeros—this is common in sensor or transactional data and can drastically change your handling strategy. Document your assumptions and test multiple approaches with cross-validation to avoid silent failures.
Investigate why nulls/zeros exist: are they missing at random, structural, or true zeros? Consult domain experts and data documentation to determine the meaning and potential impact.
Analyze the distribution of the feature and the proportion of nulls/zeros. Check if missingness correlates with other variables or the target, which could introduce bias.
Consider deletion (if missingness is low and random), imputation (mean/median/mode, model-based, or indicator variables), or transformation (e.g., log, binning). For zeros, decide if they should be treated as valid or missing.
Experiment with different strategies using cross-validation and evaluate model performance and interpretability. Monitor for data leakage and ensure the approach aligns with business goals.
Document the chosen approach, rationale, and any assumptions. Continuously monitor model performance and revisit the strategy as new data arrives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mentioned VIF and just dropping one of a correlated pair, then brought up PCA as an option if you really want to preserve variance.
Start by acknowledging that multicollinearity can harm model interpretability and stability, then outline a systematic process to identify and handle correlated features. Emphasize that the choice depends on the model type, business goal, and whether prediction or inference is prioritized. Conclude with a practical recommendation, such as using domain knowledge or regularization, and mention how you would validate the decision.
Pro tip: At Amazon, tie your answer to customer impact and scalability: explain how removing redundant features can reduce inference cost and latency, and mention that you'd use feature importance from a tree-based model or SHAP values to guide the decision.
Compute a correlation matrix (Pearson, Spearman, or VIF) to identify pairs or groups of features with high correlation (e.g., |r| > 0.8 or VIF > 5). Visualize with a heatmap to spot patterns.
Determine if the correlated features are critical for business interpretation or if they are redundant. Consider the model type: linear models suffer from multicollinearity, while tree-based models are more robust but may still benefit from feature reduction.
Use domain knowledge to keep the most interpretable or actionable feature. Alternatively, apply statistical methods like PCA, regularization (Lasso), or recursive feature elimination. For tree models, use feature importance to rank and drop less important correlated features.
Evaluate the impact of feature removal on model performance using cross-validation and business metrics. Monitor for any degradation and be prepared to reintroduce features if necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.