← Capital One Interview Insights
I knew immediately it should be logistic regression but fumbled the rigorous part.
Start by explaining why OLS is inappropriate for binary outcomes, focusing on violated assumptions and nonsensical predictions. Then introduce logistic regression as the standard alternative, detailing the logit link function, its assumptions, and diagnostic checks. Conclude with practical considerations like interpretation and model evaluation.
Pro tip: Emphasize that while logistic regression is standard, you should also mention alternatives like probit or tree-based models when appropriate, showing awareness of trade-offs. Also, highlight the importance of checking for separation or complete/quasi-complete separation, a common pitfall in logistic regression.
Discuss how OLS assumes continuous, unbounded outcomes and homoscedastic errors, which are violated with binary data. Predictions can fall outside [0,1], and the linear probability model misrepresents the relationship.
Describe logistic regression as a generalized linear model with a logit link function that maps linear predictor to probabilities in (0,1). Explain that it models the log-odds of the event.
Cover assumptions: linearity of log-odds with continuous predictors, independence of errors, no perfect multicollinearity, and large sample size for reliable estimates. Mention that homoscedasticity is not required.
Explain how to check assumptions: use Box-Tidwell for linearity, VIF for multicollinearity, residual plots (e.g., deviance residuals), and goodness-of-fit tests like Hosmer-Lemeshow. Also check for influential points and separation.
Mention metrics like AUC-ROC, confusion matrix, and calibration. Briefly note alternatives like probit, complementary log-log, or machine learning models if assumptions are violated or for prediction focus.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty classic encoding trap but I spent too long explaining it.
First, explain that treating day_of_week as a numeric feature imposes an artificial ordinal relationship and equal spacing between days, which is incorrect. Then, describe how to fix it using cyclical encoding (sine/cosine) or one-hot encoding, and finally, recommend a statistical test like ANOVA or a chi-square test to check if day matters.
Pro tip: Mention that the choice between cyclical and one-hot encoding depends on whether the day effect is expected to be smooth or discrete, and that cyclical encoding is often preferred for tree-based models to avoid dimensionality issues.
Explain that encoding day_of_week as integers 1-7 incorrectly assumes a linear, ordinal relationship and equal intervals between days, which can mislead the model.
Suggest one-hot encoding for discrete day effects or cyclical encoding (sine and cosine transformations) to preserve the cyclical nature of days.
Highlight that the choice of encoding depends on the model type; for linear models, one-hot is common, while for tree-based models, cyclical encoding can be more efficient.
Propose ANOVA if the target is continuous and day is categorical, or a chi-square test if the target is categorical, to test whether day_of_week has a significant effect.
Mention checking assumptions of the test (e.g., normality, homogeneity of variance for ANOVA) and considering post-hoc tests if the overall test is significant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Negative seat counts are just invalid so I said treat them as missing rather than trying to clip or transform.
First, diagnose the root cause and distinguish between negative values (invalid) and missing values (absent). Then, propose handling strategies for each, emphasizing that negative values should be corrected or treated as missing, while missing values require careful imputation that balances bias and variance. Finally, discuss the trade-offs of different imputation methods in terms of model performance and interpretability.
Pro tip: Always validate imputation by checking if the imputed values make sense in the business context (e.g., flight seats cannot be negative) and consider creating an indicator variable for missingness to capture potential information in the missing pattern.
Investigate the extent of negative and missing values, their patterns, and potential causes. Determine if negatives are due to data entry errors, ETL bugs, or valid business cases (e.g., refunds).
Since flight seats cannot be negative, treat them as invalid. Options: correct if possible (e.g., absolute value if sign error), set to missing, or cap at zero. Choose based on domain knowledge and impact.
Evaluate imputation methods (mean, median, mode, regression, KNN, multiple imputation) based on data type, distribution, and missingness mechanism. Consider bias-variance trade-off: simple imputation (e.g., mean) has low variance but high bias; complex methods reduce bias but may increase variance.
Explain how each method affects model bias and variance. For example, mean imputation shrinks variance and distorts relationships, while multiple imputation preserves variability but adds computational complexity.
Test the impact of chosen strategies on model performance using cross-validation. Monitor for unintended consequences and be prepared to adjust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining VIF and deriving its formula from the R-squared of regressing one predictor on the others. Then interpret the VIF of 12 in practical terms, list three remedies for multicollinearity, and discuss how standardization affects coefficient interpretation and VIF diagnostics.
Pro tip: Emphasize that VIF is a diagnostic, not a fix; and that standardization changes coefficient units but not VIF values, so it doesn't solve multicollinearity but can help with interpretation and numerical stability.
Explain that VIF measures how much the variance of an estimated regression coefficient is inflated due to multicollinearity. Derive VIF = 1/(1 - R_j^2) by considering the variance of the j-th coefficient in a multiple regression.
State that a VIF of 12 means the variance of the coefficient for turnaround_time is 12 times larger than if it were uncorrelated with other predictors. This indicates high multicollinearity, leading to unstable estimates and wide confidence intervals.
Provide three remedies: (1) remove or combine correlated predictors, (2) use regularization techniques like ridge regression or LASSO, (3) apply dimensionality reduction such as PCA.
Discuss that standardizing features (e.g., z-score) changes the scale of coefficients, making them comparable in terms of standard deviation units, but does not change VIF values because VIF is based on correlations, which are scale-invariant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with PR-AUC over ROC-AUC given the imbalance, explained why ROC can look deceptively good with rare positives.
Start by acknowledging the class imbalance and its impact on metric choice, then recommend metrics like PR-AUC, F1, and recall at a fixed precision, explaining why accuracy is misleading. For cross-validation, emphasize time-series-aware splitting (e.g., expanding window or purged K-fold with embargo) to prevent temporal leakage. Finally, discuss probability calibration methods like Platt scaling or isotonic regression, and how to validate them with reliability diagrams and Brier score.
Pro tip: Tie your metric choice to the business cost of false positives vs. false negatives—Capital One cares about financial impact, so mention how you'd align metrics with cost-sensitive decisions. Also, note that calibration should be done on a separate validation set to avoid overfitting.
Explain why accuracy is misleading for imbalanced data and recommend metrics like PR-AUC, F1, recall at high precision, and Matthews correlation coefficient. Discuss how to select based on business objectives.
Describe using expanding window or purged K-fold with embargo to respect temporal order. Highlight that standard K-fold leaks future information and inflates performance.
Discuss methods like Platt scaling (sigmoid) and isotonic regression, and the importance of using a separate calibration set. Mention that tree-based models often need calibration.
Explain how to assess calibration with reliability diagrams, Brier score, and log loss. Emphasize that calibration should be evaluated on out-of-time data to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.