K-Fold Cross Validation

K-Fold Cross Validation is a technique used to assess how well a machine learning model will generalize to an independent dataset. It works by dividing the available data into k equally sized folds or subsets. The model is then trained k times, each time using k-1 folds as the training set and the remaining fold as the validation set. The performance metrics from each of the k iterations are then averaged to provide an overall estimate of the model's performance.

Visit the following resources to learn more:

• @article@Cross-validation: evaluating estimator performance | scikit-learn
• @article@A Comprehensive Guide to K-Fold Cross Validation
• @video@Complete Guide to Cross Validation