Training-validation-test split and cross-validation done right

One crucial step in machine learning is the choice of model. A suitable model with suitable hyperparameter is the key to a good prediction result. When we are faced with a choice between models, how should the decision be made?

This is why we have cross validation. In scikit-learn, there is a family of functions that help us do this. But quite often, we see cross validation used improperly, or the result of cross validation not being interpreted correctly.

In this tutorial, you will discover the correct procedure to use cross validation and a dataset to select the best models for a project.

After completing this tutorial, you will know: