Hands-On Gradient Boosting with XGBoost and scikit-learn

5 (1 reviews total)
By Corey Wade
    Advance your knowledge in tech with a Packt subscription

  • Instant online access to over 7,500+ books and videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies
  1. Section 1: Bagging and Boosting

About this book

XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.

The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You’ll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You’ll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you’ll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines.

By the end of the book, you’ll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.

Publication date:
October 2020
Publisher
Packt
Pages
310
ISBN
9781839218354

 

Section 1: Bagging and Boosting

An XGBoost model using scikit-learn defaults opens the book after preprocessing data with pandas and building standard regression and classification models. The practical theory behind XGBoost is explored by advancing through decision trees (XGBoost base learners), random forests (bagging), and gradient boosting to compare scores and fine-tune ensemble and tree-based hyperparameters.

This section comprises the following chapters:

About the Author

  • Corey Wade

    Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.

    Browse publications by this author

Latest Reviews

(1 reviews total)
A great book on xgboost and doing efficient analysis quickly. Highly recommended.

Recommended For You

Hands-On Gradient Boosting with XGBoost and scikit-learn
Unlock this book and the full library for FREE
Start free trial