9.4: Cross-Validation
Read through this article for a brief visual summary of cross-validation.
Cross-validation is a technique for validating learning models. Up until this point in the course, model evaluations have only been applied using a single test (usually by splitting up a data set into a training set and a test set). In practice, a statistical distribution of test results must be constructed. Only then can confidence intervals be applied to the resulting distribution. Read through this article to understand cross-validation.
Work through this programming example in order to implement a cross-validation scheme on a scikit-learn data set you have seen in the previous units.
Use this project as a culminating exercise to implement the concepts presented in this unit. This exercise will show you how to obtain a data set, create the model, examine residuals, visualize results, validate the model and apply the model.