I knew accuracy was wrong immediately because of class imbalance, said so right away.
Start by acknowledging the class imbalance and the need for metrics that focus on the minority class. Then discuss precision-recall trade-offs, ranking metrics like PR-AUC, and cost-sensitive evaluation, explaining why accuracy and ROC-AUC can be misleading in rare-event detection.
Pro tip: Emphasize that the choice of metric should align with the business cost of false positives vs. false negatives, and mention that in practice, you might use a combination of metrics and set a threshold based on the desired precision or recall.
State that rare events lead to highly imbalanced datasets, making accuracy a poor metric because a model can achieve high accuracy by always predicting the majority class.
ROC-AUC can be overly optimistic because it considers both true positive rate and false positive rate, but in imbalanced settings, the false positive rate can be low even with many false positives, and it doesn't reflect the precision-recall trade-off.
Recommend using precision, recall, F1-score, and especially the Precision-Recall AUC (PR-AUC) because they focus on the positive (rare) class and are more informative when the negative class dominates.
Mention that depending on the application, you might use cost-sensitive metrics (e.g., expected cost) or ranking metrics like lift, and that you should choose a threshold based on the desired balance of precision and recall.
Conclude that the metric should reflect the real-world costs of false positives and false negatives, and that you might combine multiple metrics for a comprehensive evaluation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that standard normal approximations fail with small positive counts, then propose exact or Bayesian methods. Walk through modeling each model's performance with a binomial or beta-binomial distribution, constructing intervals via Clopper-Pearson or Jeffreys, and testing differences with Fisher's exact test or a Bayesian posterior probability. Emphasize that the choice depends on assumptions and the decision context.
Pro tip: Mention that in safety-critical domains like autonomous driving, you should pre-register the analysis plan and consider Bayesian methods with informative priors to stabilize estimates, while also reporting frequentist results for robustness.
Define what 'positive examples' mean (e.g., successful detections) and state assumptions like independence and identical distribution. Discuss whether the small sample is due to rarity or data collection limits.
Model each model's positive count as Binomial(n, p) or use a Beta-Binomial if overdispersion is present. For very small counts, consider Poisson or negative binomial if events are rare.
Use exact methods like Clopper-Pearson for binomial proportions, or Bayesian credible intervals with a Beta prior (e.g., Jeffreys). Avoid Wald intervals due to poor coverage.
Apply Fisher's exact test for 2x2 tables, or a Bayesian test computing the posterior probability that one model is better. For paired data, use McNemar's test.
Report effect sizes with intervals, discuss practical significance, and acknowledge limitations. Recommend sequential testing or Bayesian decision rules if data is limited.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge the data limitation upfront, then propose a principled approach that combines uncertainty quantification (e.g., Bayesian methods or bootstrapping) with domain constraints. Emphasize that with small samples, the goal shifts from maximizing accuracy to making robust, conservative decisions that respect alert budgets and safety requirements.
Pro tip: Show that you understand the operational context: at Waymo, false negatives can be catastrophic, so you might prioritize recall over precision and use a threshold that minimizes missed detections, even if it means more false alerts—but then manage the alert budget by tiering or deferring low-confidence cases.
Use Bayesian credible intervals or bootstrap resampling to estimate the uncertainty around any metric. Communicate that with small samples, point estimates are unreliable and decisions should be based on conservative bounds.
Define the relative cost of false positives vs. false negatives and the hard limit on alert volume. This turns the problem into a constrained optimization: maximize detection under the alert budget.
Prefer simple, well-calibrated models (e.g., logistic regression) and use cross-validation or holdout with uncertainty. For threshold selection, use cost-sensitive learning or optimize a metric like F-beta that weights recall higher.
Assess calibration via reliability diagrams or metrics like Brier score, but acknowledge small-sample noise. Consider using isotonic regression or Platt scaling with regularization, and validate on held-out data if possible.
Make a decision that is defensible given uncertainty, and set up a monitoring plan to collect more data and re-evaluate. If possible, use a human-in-the-loop or tiered alerting to mitigate risk.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.