Evaluating Machine Learning Models

Data science today is a lot like the Wild West: there's endless opportunity and excitement, but also a lot of chaos and confusion. If you're new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O'Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics. In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners. With this report, you will: Learn the stages involved when developing a machine-learning model for use in a software application Understand the metrics used for supervised learning models, including classification, regression, and ranking Walk through evaluation mechanisms, such as hold?out validation, cross-validation, and bootstrapping Explore hyperparameter tuning in detail, and discover why it's so difficult Learn the pitfalls of A/B testing, and examine a promising alternative: multi-armed bandits Get suggestions for further reading, as well as useful software packages.