What is cross-validation and how is it used in machine learning?

Cross-validation is a technique used in machine learning to evaluate the performance of a model on new, unseen data. It involves splitting the available data into multiple subsets, or "folds," and training the model on one subset while using the other subsets to evaluate the model's performance.

The most common type of cross-validation is k-fold cross-validation, where the available data is split into k equal-sized folds. The model is then trained on k-1 folds and tested on the remaining fold. This process is repeated k times, with each fold used as the test set exactly once. The results are then averaged across the k-folds to obtain an estimate of the model's performance on new, unseen data.

Cross-validation is used in machine learning to estimate the performance of a model on new, unseen data and to tune the model's hyperparameters. Hyperparameters are parameters that are set before the model is trained, such as the learning rate or regularization strength. Tuning these hyperparameters can improve the performance of the model on new data, and cross-validation provides a way to evaluate the performance of different hyperparameter settings.

Cross-validation is important because it provides a more accurate estimate of a model's performance on new, unseen data than simply evaluating the model on the training data. By evaluating the model on multiple subsets of the data, cross-validation provides a more robust estimate of the model's generalization performance.