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 4: From Gradient Boosting to XGBoost

XGBoost is a unique form of gradient boosting with several distinct advantages, which will be explained in Chapter 5, XGBoost Unveiled. In order to understand the advantages of XGBoost over traditional gradient boosting, you must first learn how traditional gradient boosting works. The general structure and hyperparameters of traditional gradient boosting are incorporated by XGBoost. In this chapter, you will discover the power behind gradient boosting, which is at the core of XGBoost.

In this chapter, you will build gradient boosting models from scratch before comparing gradient boosting models and errors with previous results. In particular, you will focus on the learning rate hyperparameter to build powerful gradient boosting models that include XGBoost. Finally, you will preview a case study on exoplanets highlighting the need for faster algorithms, a critical need in the world of big data that is satisfied by XGBoost.

In this...