Название: XGBoost With Python: Gradient Boosted Trees With XGBoost and scikit-learnАвтор: Jason Brownlee
Издательство: Machine Learning Mastery Pty. Ltd.
Год: 2018
Страниц: 115
Язык: английский
Формат: pdf (true), djvu
Размер: 22.9 MB
Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects. Gradient Boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions. This is my goal for you and this book is your ticket to that outcome.
The tutorials in this book are divided into three parts:
- XGBoost Basics.
- XGBoost Advanced.
- XGBoost Tuning.
In addition to these three parts, the Conclusions part at the end of the book includes a list of resources for getting help and diving deeper into the field.
Why Is XGBoost So Powerful?
… the secret is its “speed” and “model performance”
Building up a catalog of code recipes is an important part of your XGBoost journey. Each time you learn about a new technique or new problem type, you should write up a short code recipe that demonstrates it. This will give you a starting point to use on your next machine learning project.
As part of this book you will receive a catalog of XGBoost recipes. This includes recipes for all of the tutorials presented in this book. You are strongly encouraged to add to and build upon this catalog of recipes as you expand your use and knowledge of XGBoost in Python.
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