person Author: Process Fellows
K-fold cross-validation is a common technique in machine learning used to evaluate model performance. It involves dividing the dataset into k equally sized folds. In each of the k iterations, one fold is used as the validation set while the remaining k–1 folds are used for training. This process is repeated k times, with each fold used exactly once as the validation set. The performance results from each iteration are then averaged to produce a final estimate. The value of k is a predefined parameter, typically set to 10, meaning the dataset is split into 10 parts.