A collection of techniques used to assess how well a predictive model will generalize to new data sets.
- k-fold Cross-Validation: A method where the data is divided into k subsets, and the model is trained on k-1 subsets while being tested on the remaining subset. This process is repeated k times, with each subset serving as the test set once.
- Leave-p-out Cross-Validation: A variation of cross-validation where p data points are left out for testing, and the model is trained on the remaining data. This process is repeated for all possible combinations of the data points.