Cross-Validation

While a simple train-test split is a good first step, its evaluation can be sensitive to how the data was split. By chance, the test set might contain particularly easy or difficult examples, leading to an overly optimistic or pessimistic performance estimate. Cross-Validation (CV) is a more robust technique that mitigates this problem by using multiple train-test splits. The most common form of cross-validation is k-fold cross-validation. In this method, the dataset is randomly partitioned into 'k' equal-sized subsets, or 'folds'. The model is then trained and evaluated 'k' times. In each iteration, one of the folds is used as the testing set, and the remaining k-1 folds are combined to form the training set. This process is repeated until every fold has been used as a test set exactly once. The final performance metric is then the average of the metrics from all 'k' iterations. For example, in 5-fold cross-validation, the data is split into 5 folds. The model is trained on folds 1-4 and tested on fold 5, then trained on folds 1,2,3,5 and tested on fold 4, and so on. This approach provides a more stable and reliable estimate of the model's performance on unseen data because it uses the entire dataset for both training and testing across the different iterations. It's a standard procedure for model selection and hyperparameter tuning.