Cross Validation and Tuning

Cross Validation and Tuning

Now that we've covered supervised modeling with pipelines in Feature Engineering and Pipelines and practiced Scikit-learn basics in Machine Learning Made Easy: Scikit-Learn, we are ready for the final supervised modeling habit: running fair experiments.

A single train/test split is useful, but it can be lucky or unlucky. Cross validation helps us ask a better question:

If this model saw several different training samples, would it still perform well?

Then tuning helps us choose model settings like tree depth, regularization strength, or number of neighbors without manually guessing. This is the workflow software engineers should recognize: run repeatable experiments, compare candidates, and keep the final test set clean.