In this notebook, we presented the framework used in machine-learning to evaluate a predictive model’s performance: the cross-validation.
Besides, we presented several splitting strategies that can be used in the general cross-validation framework. These strategies should be used wisely when encountering some specific patterns or types of data.
Finally, we show how to perform nested cross-validation to select an optimal model and evaluate its generalization performance.
To go further#
You can refer to the following scikit-learn examples which are related to the concepts approached in this module: