Hands-On Gradient Boosting with XGBoost and scikit-learn
This practical XGBoost guide will put your Python and scikit-learn knowledge to work by showing you how to build powerful, fine-tuned XGBoost models with impressive speed and accuracy. This book will help you to apply XGBoost’s alternative base learners, use unique transformers for model deployment, discover tips from Kaggle masters, and much more!
Offered by
Difficulty Level
Intermediate
Completion Time
10h20m
Language
English
About Book
Who Is This Book For?
This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.
Hands-On Gradient Boosting with XGBoost and scikit-learn
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 10h20m total length
Machine Learning Landscape
Decision Trees in Depth
Bagging with Random Forests
From Gradient Boosting to XGBoost
XGBoost Unveiled
XGBoost Hyperparameters
Discovering Exoplanets with XGBoost
XGBoost Alternative Base Learners
XGBoost Kaggle Masters
XGBoost Model Deployment
Related Resources
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