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 8: XGBoost Alternative Base Learners

In this chapter, you will analyze and apply different base learners in XGBoost. In XGBoost, base learners are the individual models, most commonly trees, that are iterated upon for each boosting round. Along with the default decision tree, which XGBoost defines as gbtree, additional options for base learners include gblinear and dart. Furthermore, XGBoost has its own implementations of random forests as base learners and as tree ensemble algorithms that you will experiment with in this chapter.

By learning how to apply alternative base learners, you will greatly extend your range with XGBoost. You will have the capacity to build many more models and you will learn new approaches to developing linear, tree-based, and random forest machine learning algorithms. The goal of the chapter is to give you proficiency in building XGBoost models with alternative base learners so that you can leverage advanced XGBoost options to find the best possible...