Choice of cross-validationΒΆ

In the previous notebooks, we presented the cross-validation framework. However, we always used either a default KFold or a ShuffleSplit strategies to iteratively split our dataset. However, you should not assume that these approaches are always the best option: some other cross-validation strategies might be better adapted to your problem. Indeed, we will focus on three aspects that influenced the choice of the cross-validation strategy: class stratification, sample grouping, and feature dependence.