Summary

In this chapter, we tried to complement our existing machine learning skills by discussing best practices in model selection and hyperparameter tuning. You learned how to tweak the hyperparameters of a model using grid search and cross-validation in both OpenCV and scikit-learn. We also talked about a wide variety of evaluation metrics and how to chain algorithms into a pipeline.

Now you are almost ready to start working on some real-world problems on your own. But before we part ways, the next chapter will give you some tips and tricks on how to approach a machine learning problem in the wild.