Evaluating Machine Learning Models
A Beginner's Guide to Key Concepts and Pitfalls
By Alice Zheng
Released: September 2015
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:
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.