Название: Machine Learning With Boosting: A Beginner's Guide
Автор: Scott Hartshorn
Издательство: Amazon.com Services LLC
Год: 2017
Страниц: 226
Язык: английский
Формат: pdf, azw3, epub
Размер: 14.0 MB

Machine Learning - Made Easy To Understand.

If you are looking for a book to help you understand how the machine learning algorithm “Gradient Boosted Trees”, also known as “Boosting”, works behind the scenes, then this is a good book for you. Boosting is a widely used algorithm in a variety of applications, including big data analysis for industry and data analysis competitions like you would find on Kaggle. Boosting has, in fact, become one of the dominant winning algorithms on Kaggle.

This book explains how Decision Trees work and how they can be used sequentially to reduce many of the common problems with decision trees, such as overfitting the training data. That method is known is Gradient Boosted Trees.

This is an example driven book, rather than a theory driven book. That means we will be showing the actual algorithms within the code that executes gradient boosted trees, instead of showing the high level equations about which loss functions are being optimized.








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