Cross-validation using the k-fold method is a technique to evaluate the performance of a machine learning model. In this approach, the dataset is divided into ‘k’ subsets or folds of equal size. The model is trained on ‘k-1’ folds and validated on the remaining one, and this process is repeated ‘k’ times, each time using a different fold for validation. The results are then averaged to obtain a single performance metric.
For instance, in a 5-fold cross-validation, the data is split into 5 parts. The model is trained on 4 of these parts and validated on the fifth. This procedure is repeated 5 times, with each part being the validation set once. The final performance metric is the average of the metrics obtained in each of the 5 validation steps, providing a robust assessment of the model’s generalization capability.