Generalization and Evaluation — Interview Grill

50 questions on data leakage, calibration, distribution shift, class imbalance, ablations. Drill until you can answer 35+ cold.

A. Data leakage

1. Define data leakage. Information from outside the training set leaking in, inflating offline metrics that don't transfer.

2. Four types of leakage? Target leakage, train-test contamination, preprocessing leakage, temporal leakage. (Group leakage is a fifth special case.)

3. Example of target leakage in churn prediction? Using "days since last login" as a feature when it's computed after churn. The future-looking feature trivially predicts churn.

4. Why is random k-fold wrong for time series? Allows training on the future, predicting the past. Use time-series split (sliding/expanding window) instead.

5. Standardize features using overall mean — leakage? Yes. Test data influences train preprocessing. Compute mean/std on train only.

6. Same user in train and test — leakage? If predicting user-level outcomes and the model learns user-specific patterns: yes. Use group k-fold.

7. How do you detect leakage post-hoc? Sky-high offline metrics that don't translate to deployment. Check feature importance for "too good to be true" features. Audit: could each feature have been computed at prediction time?

B. Calibration

8. What does calibrated mean? Predictions match observed frequencies: among predictions of 0.7, ~70% are positive.

9. How do you measure calibration? Reliability diagram (bin predictions; plot empirical positive rate vs predicted score). ECE: average gap between confidence and accuracy across bins.

10. ECE formula? $\mathrm{ECE}={\sum }_b\frac{|B_b|}{N}|\mathrm{acc}(B_b)-\mathrm{conf}(B_b)|$.

11. Brier score? $\frac{1}{N}\sum (\hat{p}-y{)}^2$. Combined calibration + resolution.

12. Three calibration techniques? Platt scaling (logistic), isotonic regression (non-parametric monotonic), temperature scaling (single scalar on logits).

13. Why is temperature scaling popular for deep nets? Single scalar — minimal overfitting risk. Doesn't change ranking → AUC unchanged. Standard fix for overconfident neural networks.

14. Does temperature scaling change AUC? No. Monotonic transform of scores doesn't change pairwise ordering.

15. AUC vs calibration — different things? AUC measures ranking. Calibration measures whether scores are accurate probabilities. A model can have high AUC and bad calibration.

C. Distribution shift

16. Three types of distribution shift? Covariate shift ($p(x)$ changes), label shift ($p(y)$ changes), concept drift ($p(y|x)$ changes).

17. Covariate shift — typical example? New user demographics. Input distribution shifts; the underlying relationship is the same.

18. Label shift example? Disease prevalence increases during a pandemic; the conditional symptom-given-disease is unchanged.

19. Concept drift example? User preferences evolving over time — same input features, but the label given those features changes ($p(y|x)$ shifts).

20. How do you detect input drift in production? KS test, KL divergence, PSI between train and live distributions. Monitor input feature distributions per feature.

21. What's PSI? Population Stability Index. Bin-based comparison of two distributions; PSI > 0.25 typically flagged as significant shift.

22. Importance weighting for covariate shift? Reweight training samples by $q(x)/p(x)$. Hard to estimate ratio; can use density estimators or classifier-based estimates.

23. Shift detection via classifier? Train a classifier to distinguish "is this from train or production?" If AUC > 0.5 + something, there's shift.

D. Class imbalance

24. Why is accuracy bad for imbalanced data? Predicting majority class always gives high accuracy (e.g., 99% if positive class is 1%).

25. Right metrics for rare-class problems? Precision, recall, F1, AUPRC. AUROC is OK but can be misleading at extreme imbalance.

26. AUPRC vs AUROC — when prefer AUPRC? Severely imbalanced data. AUPRC focuses on positive class behavior; AUROC averages across the full operating curve where the negative dominance dilutes.

27. SMOTE — what does it do? Synthetic minority oversampling. Generates synthetic minority points by interpolating between minority neighbors. Risk: amplifies noise/outliers near class boundaries.

28. Class weighting in the loss? Multiply per-sample loss by class-dependent weight. Standard PyTorch: nn.CrossEntropyLoss(weight=class_weights).

29. Focal loss formula? $-(1-p_t{)}^{\gamma }\log p_t$. Down-weights easy examples ($p_t$ near 1). $\gamma$ typically 2.

30. Should you resample the test set? No. Resample only training set. Test set must reflect deployment distribution.

31. After resampling, what about probabilities? They're distorted. Calibrate after, or apply a post-hoc shift to recover original ratios.

E. Bias-variance and double descent

32. Bias-variance decomposition? $\mathbb{E}[(\hat{f}(x)-y{)}^2]={\mathrm{Bias}}^2+\mathrm{Var}+{\sigma }^2$.

33. High bias means? Underfitting. Model too simple. Both train and test error high.

34. High variance means? Overfitting. Model too complex. Train error low, test error high.

35. What's double descent? Past the interpolation threshold (params ≈ data points), test error decreases again as capacity increases. Belkin et al., 2019.

36. Why does double descent happen? Modern over-parameterized models effectively select smoother interpolators. Implicit bias of optimization (SGD favors flat minima) plays a role.

37. Implicit regularization of SGD? SGD tends to converge to flat minima (low Hessian eigenvalues), which generalize better empirically. Adam has weaker implicit regularization.

F. Cross-validation

38. What's nested CV? Outer loop: k-fold for evaluation. Inner loop: k-fold within each train fold for hyperparameter tuning. Prevents tuning from leaking into evaluation.

39. Why do hyperparam tuning + evaluation on the same fold leak? You're choosing the model that does best on the eval set, biasing the eval estimate.

40. Stratified k-fold — when? Classification with imbalanced classes. Preserves class ratio per fold; reduces estimator variance.

41. Time-series CV strategy? Sliding window or expanding window. Always test on data later than train. Never random split.

42. Group k-fold use case? When you want generalization across entities (users, patients, etc.). Each entity entirely in one fold.

G. Ablations

43. What's a good ablation? One component varied at a time, everything else fixed; multiple seeds; matched compute; multiple evals if claiming generality.

44. You added X, performance improved by 0.3 points. Real? Need: multiple seeds, std reported, paired test or bootstrap of difference. 0.3 might be within seed noise.

45. Why does ablation matter when papers report single numbers? Because single numbers without ablation can't establish which component drove the gain. Component might be a placebo.

H. Metric uncertainty

46. Wald CI for accuracy? $\hat{p}\pm 1.96\sqrt{\hat{p}(1-\hat{p})/n}$.

47. CI for AUC? Bootstrap (resample test set 1000+ times, compute AUC each time, quantile). Or DeLong's method.

48. Two CIs overlap — does that mean no difference? No. Paired test on the difference is the right way. CIs of differences can exclude zero even when individual CIs overlap.

49. Paired bootstrap procedure for model comparison? For each bootstrap sample, compute metric for both models on the same sample. Look at the distribution of differences. Reject "no difference" if 0 not in CI of differences.

50. McNemar's test — when? Comparing two binary classifiers on the same test set. Tests if their disagreements are symmetric (same number of A-correct B-wrong vs A-wrong B-correct).

Quick fire

51. Best metric for fraud (rare positive)? AUPRC + cost-sensitive threshold. 52. Calibrate after rebalancing? Yes, always. 53. Split for time-series? Time-based, never random. 54. Default split for classification? Stratified. 55. Bootstrap iterations? 1000+ typical. 56. Temperature scaling change AUC? No. 57. Common shift detection metric? PSI, KS test. 58. Brier score lower = better? Yes. 59. ECE measures? Calibration error. 60. Model selection on test set? Don't.

Self-grading

If you can't answer 1-15, you'll get fooled by your own metrics. If you can't answer 16-35, you'll deploy broken systems. If you can't answer 36-50, frontier-lab evaluation rigor questions will go past you.

Aim for 40+/60 cold.