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

Alice Zheng is the Director of Data Science at Dato, a Seattle-based startup that offers

powerful large-scale machine learning and graph analytics tools. A tool builder and an

expert in machine-learning algorithms, her research spans software diagnosis, computer

network security, and social network analysis.