Advanced Cross-Validation

Advanced cross-validation techniques, such as k-fold and leave-one-out, provide more robust and accurate assessments of model performance in ML. These methods go beyond the basic train-test split and allow for a more comprehensive evaluation of model generalization.

The k-fold cross-validation technique

The k-fold cross-validation technique involves dividing the original dataset into k equally sized subsets or folds. The model is trained and evaluated k times, each time using a different fold as the test set and the remaining folds as the training set. The performance metrics obtained from each fold are then averaged to obtain an overall assessment of the model’s performance.