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


In this chapter, you have taken a big leap toward mastering XGBoost by examining decision trees, the primary XGBoost base learners. You built decision tree regressors and classifiers by fine-tuning hyperparameters with GridSearchCV and RandomizedSearchCV. You visualized decision trees and analyzed their errors and accuracy in terms of variance and bias. Furthermore, you learned about an indispensable tool, feature_importances_, which is used to communicate the most important features of your model that is also an attribute of XGBoost.

In the next chapter, you will learn how to build Random Forests, our first ensemble method and a rival of XGBoost. The applications of Random Forests are important for comprehending the difference between bagging and boosting, generating machine learning models comparable to XGBoost, and learning about the limitations of Random Forests that facilitated the development of XGBoost in the first place.