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

Chapter 3: Bagging with Random Forests

In this chapter, you will gain proficiency in building random forests, a leading competitor to XGBoost. Like XGBoost, random forests are ensembles of decision trees. The difference is that random forests combine trees via bagging, while XGBoost combines trees via boosting. Random forests are a viable alternative to XGBoost with advantages and limitations that are highlighted in this chapter. Learning about random forests is important because they provide valuable insights into the structure of tree-based ensembles (XGBoost), and they allow a deeper understanding of boosting in comparison and contrast with their own method of bagging.

In this chapter, you will build and evaluate random forest classifiers and random forest regressors, gain mastery of random forest hyperparameters, learn about bagging in the machine learning landscape, and explore a case study that highlights some random forest limitations that spurred the development of gradient...