Crossvalidation

Cross-validation is a statistical method used to assess how the results of a predictive model will generalize to an independent dataset. It provides an estimate of model performance on unseen data and supports model comparison and hyperparameter tuning while reducing overfitting.

The basic approach is the hold-out method, which splits the data into a training set and a separate test set. More robust estimates are obtained with k-fold cross-validation, where the data are divided into k folds. The model is trained on k−1 folds and evaluated on the remaining fold, with the process repeated k times and the results averaged. Stratified k-fold preserves the class distribution in classification tasks. Leave-one-out cross-validation uses every observation once as a test instance, which can yield low bias but high variance and substantial computational cost. Repeated or Monte Carlo cross-validation performs several random splits to stabilize the estimate.

Nested cross-validation combines an inner loop for model selection or hyperparameter tuning with an outer loop for unbiased performance estimation, helping to prevent optimistic bias in reported results.

Common choices of k balance bias and variance; 5 or 10 folds are typical. LOOCV may be impractical for large datasets. When data are time-ordered, conventional cross-validation can leak temporal structure; techniques such as forward chaining or rolling-origin forecasting are used.

Practical considerations include standardizing or scaling within folds, maintaining data leakage avoidance, and using stratification for imbalanced tasks. The outcomes of cross-validation are performance estimates (for example accuracy, AUC, RMSE) rather than final measurements on a truly independent test set. Cross-validation is a versatile tool in model evaluation and selection, complementary to a separate hold-out test set for final assessment.