Evaluating Machine Learning Models

A Beginner's Guide to Key Concepts and Pitfalls

Evaluating Machine Learning Models

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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

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Alice Zheng

Alice Zheng

Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.