What is K-fold validation in machine learning?

 K-fold cross-validation is a technique used in machine learning to evaluate the performance of a model. The basic idea of K-fold cross-validation is to split the available data into K equally sized "folds" or subsets, then train and evaluate the model K times, each time using a different fold as the validation set and the remaining K-1 folds as the training set.

The process can be summarized as follows:

1. Divide the dataset into K equal parts (or "folds").
2. For each fold, use that fold as the validation set and the remaining K-1 folds as the training set.
3. Train the model on the training set and evaluate it on the validation set.
4. Record the performance metric (e.g. accuracy, precision, recall, etc.) for that fold.
5. Repeat steps 2-4 K times, using a different fold as the validation set each time.
6. Calculate the average performance metric across all K folds to get a more reliable estimate of the model's performance.

K-fold cross-validation is a useful technique for evaluating a model's performance because it can provide a more accurate estimate of the model's generalization error, i.e. how well the model will perform on new, unseen data. By using multiple validation sets, K-fold cross-validation reduces the risk of overfitting the model to the training data and can help identify potential sources of bias in the model.