Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By : Corey Wade
Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By: Corey Wade

Overview of 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.
Table of Contents (15 chapters)
Section 1: Bagging and Boosting
Section 2: XGBoost
Section 3: Advanced XGBoost

Exploring Kaggle competitions

"I used only XGBoost (tried others but none of them performed well enough to end up in my ensemble)."

Qingchen Wang, Kaggle Winner


In this section, we will investigate Kaggle competitions by looking at a brief history of Kaggle competitions, how they are structured, and the importance of a hold-out/test set as distinguished from a validation/test set.

XGBoost in Kaggle competitions

XGBoost built its reputation as the leading machine learning algorithm on account of its unparalleled success in winning Kaggle competitions. XGBoost often appeared in winning ensembles along with deep learning models such as neural networks, in addition to winning outright. A sample list of XGBoost Kaggle competition winners appears on the Distributed (Deep) Machine Learning Community web page at For...