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Felt solid here, probably the most rehearsed topic I had going in.
Start by defining bias and variance and the trade-off between them, then explain how underfitting corresponds to high bias and overfitting to high variance. Use a concrete example (e.g., polynomial regression) to illustrate the concepts and discuss practical strategies to balance the trade-off.
Pro tip: Relate the trade-off to real-world engineering decisions, such as choosing model complexity based on data size and business impact, and mention that sometimes a slightly biased model is preferred for interpretability or latency.
Explain bias as error from erroneous assumptions (e.g., linear model on nonlinear data) and variance as sensitivity to small fluctuations in the training set.
Describe how increasing model complexity reduces bias but increases variance, and vice versa, leading to an optimal point that minimizes total error.
Underfitting occurs when the model is too simple (high bias, low variance), while overfitting occurs when the model is too complex (low bias, high variance).
Use a polynomial regression example: a linear fit underfits, a high-degree polynomial overfits, and a moderate degree balances the trade-off.
Mention techniques like cross-validation, regularization (L1/L2), early stopping, and ensemble methods to manage the trade-off.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The imbalanced part is where it got interesting.
Start by defining each metric and its focus (accuracy: overall correctness; precision: false positive avoidance; recall: false negative avoidance; F1: balance). Then explain how class imbalance makes accuracy misleading and shifts the choice toward precision, recall, or F1 based on the business cost of errors. Finally, discuss practical strategies like resampling, class weights, and threshold tuning.
Pro tip: Tie your metric choice to the business context—e.g., in fraud detection, recall is critical to catch fraud, but precision matters to avoid annoying customers. Mention that you'd also monitor precision-recall AUC and consider the cost matrix.
Briefly define accuracy, precision, recall, and F1, highlighting what each measures and its focus (overall correctness vs. false positives vs. false negatives vs. balance).
Explain scenarios: accuracy for balanced classes and equal error costs; precision when false positives are costly; recall when false negatives are costly; F1 when you need a balance and have uneven class distribution.
Discuss how accuracy becomes misleading with imbalanced classes (e.g., 99% negative class yields 99% accuracy by predicting all negative). Emphasize that precision, recall, and F1 are more informative.
Recommend selecting based on business costs: use recall if missing positives is costly, precision if false alarms are costly, or F1 for a balance. Mention alternatives like PR-AUC, ROC-AUC, and Matthews correlation coefficient.
Describe techniques to handle imbalance: resampling (oversampling/undersampling), class weights, threshold tuning, and using appropriate evaluation metrics. Stress the importance of validating with a hold-out set or cross-validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the frequentist vs Bayesian framing.
Start by defining a confidence interval (CI) as a frequentist interval estimate that would contain the true parameter in a specified proportion of repeated samples. Contrast it with a Bayesian credible interval (CrI), which is a posterior probability interval given the observed data and a prior. Then, for classifier accuracy, describe constructing a CI using the normal approximation (Wald) or Wilson score interval, and mention how to handle small samples or imbalanced data.
Pro tip: Emphasize that the CI does not give the probability that the true accuracy lies in the interval, while the CrI does—this distinction often trips up candidates. Also, mention that for classifier accuracy, the Wilson interval is preferred over the normal approximation because it performs better for small samples and extreme proportions.
Explain that a CI is a range of values, derived from sample data, that is likely to contain the true population parameter with a certain confidence level (e.g., 95%). Clarify that the confidence level refers to the long-run frequency of intervals containing the parameter, not the probability for a specific interval.
Describe a credible interval as a range of values within which the true parameter lies with a given posterior probability, based on the observed data and a prior distribution. Highlight that it directly answers the question 'What is the probability the parameter is in this interval?'
Compare the two: CIs are based on frequentist probability and treat the parameter as fixed; CrIs are based on Bayesian probability and treat the parameter as random. Mention that CrIs require a prior, while CIs do not.
For a classifier's accuracy, treat correct predictions as Bernoulli trials. Use the sample proportion (accuracy) and construct a CI. For large samples, use the normal approximation: p_hat ± z * sqrt(p_hat*(1-p_hat)/n). For small samples or extreme proportions, use the Wilson score interval or Clopper-Pearson exact interval.
Mention that for imbalanced datasets, accuracy may not be the best metric; consider confidence intervals for precision, recall, or F1. Also, note that if the classifier is evaluated on a test set, the CI reflects uncertainty due to sampling variability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.