Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and PitfallsКНИГИ » ПРОГРАММИНГ
Название: Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and Pitfalls Автор: 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
Introduction to Machine Learning with Python Название: Introduction to Machine Learning with Python (Early Release) Автор: Andreas C. Mueller, Sarah Guido Издательство: O'Reilly Media Год: 2016...
Информация
Посетители, находящиеся в группе Гости, не могут оставлять комментарии к данной публикации.
Навигация
Вход на сайт
Реклама
Evaluating Machine Learning Models: A Beginner's Guide to Key Concepts and Pitfalls 
