Название: Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and PitfallsАвтор: Alice Zheng
Издательство: O'Reilly Media
Год: 2015
ISBN: 9781491932469
Формат: pdf
Страниц: 58
Размер: , mb
Язык: English
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