I knew the broad strokes but fumbled the variance explanation a bit.
Start by defining both methods and their computational costs, then analyze the bias-variance tradeoff of the error estimate, and finally give a practical recommendation based on dataset size, model training cost, and variance considerations.
Pro tip: Mention that for large datasets, the difference between LOO and 5-fold is often negligible, but LOO can be prohibitively expensive; for small datasets, LOO may be preferred despite higher variance because it uses almost all data for training.
Briefly explain that in k-fold CV, the data is split into k folds, each used once as validation, while LOO is k-fold with k=N (each sample is a validation set).
Compare training times: k-fold requires k model fits, LOO requires N model fits, which is often much larger. Mention that for some models (e.g., linear regression), LOO can be computed efficiently via closed-form updates.
Discuss that LOO has lower bias because it trains on almost all data, but higher variance because the training sets are highly correlated and the estimate can be unstable. k-fold (e.g., 5-fold) has slightly higher bias but lower variance, leading to a better bias-variance tradeoff in practice.
Recommend 5-fold or 10-fold CV for most cases due to computational efficiency and lower variance. Use LOO only for very small datasets or when computational cost is not an issue and a nearly unbiased estimate is critical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.