Description: Nested cross-validation is a robust method for estimating a model’s ability on unseen data, using two layers of cross-validation. In the first layer, the dataset is divided into several partitions, where each partition is used as a test set in different iterations, while the remaining data is used to train the model. This process allows for evaluating the model’s ability to generalize to unseen data. In the second layer, hyperparameter tuning is performed, where the model’s parameters are adjusted based on the results obtained in the first layer. This technique is particularly valuable in situations where overfitting is a concern and a more accurate estimate of model performance is sought. Nested cross-validation is widely used in machine learning and statistics, as it provides a more reliable evaluation of models, allowing practitioners to select the most suitable model for their specific needs.
