I went straight into k-fold and explained how you rotate the holdout fold each time to get a more stable estimate of generalization error.
Start by clearly defining cross-validation and its purpose in model evaluation, then explain how it is applied specifically to logistic regression, including any nuances like stratification. Finally, contrast it with a standard train/test split, highlighting differences in bias-variance trade-off, data efficiency, and reliability of performance estimates.
Pro tip: Mention that for logistic regression, stratified k-fold cross-validation is often preferred to maintain class balance, especially with imbalanced datasets, and that cross-validation can also be used for hyperparameter tuning via grid search.
Explain that cross-validation involves partitioning the data into k folds, training the model on k-1 folds and validating on the remaining fold, repeating this process k times. This provides a more robust estimate of model performance.
Describe how logistic regression is trained and evaluated in each fold, using metrics like accuracy, precision, recall, or AUC. Mention that stratification is often used to preserve class proportions in each fold.
Explain that a standard train/test split involves a single random division of data into training and test sets, which can lead to high variance in performance estimates, especially with small datasets.
Discuss how cross-validation uses all data for both training and validation, reduces variance, and provides a more reliable estimate of generalization performance. It also helps in detecting overfitting.
Mention computational cost: cross-validation is more expensive as it requires training k models. Also note that for large datasets, a single train/test split might suffice, but cross-validation is preferred for smaller datasets or when tuning hyperparameters.
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