Cross validation involves dividing the data set into multiple parts, or "folds". The model is trained on a subset of the data and tested on the remaining part. This process is repeated multiple times, with different subsets used for training and testing each time. The results are then averaged to provide a comprehensive assessment of the model’s performance. Common techniques include:
- K-Fold Cross Validation: The data set is divided into 'k' folds, and the model is trained and tested 'k' times, each time using a different fold as the test set. - Leave-One-Out Cross Validation: Each data point is used once as a test set while the remaining data points form the training set. - Stratified Cross Validation: Ensures that each fold has a proportional representation of each class, which is particularly useful for imbalanced data sets.