Python Data Science Essentials - Third Edition

Cross-validation

If you have run the previous experiment, you may have realized that:

• Both the validation and test results vary, as their samples are different.
• The chosen hypothesis is often the best one, but this is not always the case.

Unfortunately, relying on the validation and testing phases of samples brings uncertainty, along with a reduction of the learning examples dedicated to training (the fewer the examples, the more the variance of the estimates from the model).

A solution would be to use cross-validation, and Scikit-learn offers a complete module for cross-validation and performance evaluation (sklearn.model_selection).

By resorting to cross-validation, you'll just need to separate your data into a training and test set, ...