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)
1
Section 1: Bagging and Boosting
6
Section 2: XGBoost
10
Section 3: Advanced XGBoost

Chapter 1: Machine Learning Landscape

Welcome to Hands-On Gradient Boosting with XGBoost and Scikit-Learn, a book that will teach you the foundations, tips, and tricks of XGBoost, the best machine learning algorithm for making predictions from tabular data.

The focus of this book is XGBoost, also known as Extreme Gradient Boosting. The structure, function, and raw power of XGBoost will be fleshed out in increasing detail in each chapter. The chapters unfold to tell an incredible story: the story of XGBoost. By the end of this book, you will be an expert in leveraging XGBoost to make predictions from real data.

In the first chapter, XGBoost is presented in a sneak preview. It makes a guest appearance in the larger context of machine learning regression and classification to set the stage for what's to come. 

This chapter focuses on preparing data for machine learning, a process also known as data wrangling. In addition to building machine learning models, you will learn about using efficient Python code to load data, describe data, handle null values, transform data into numerical columns, split data into training and test sets, build machine learning models, and implement cross-validation, as well as comparing linear regression and logistic regression models with XGBoost.

The concepts and libraries presented in this chapter are used throughout the book.

This chapter consists of the following topics:

  • Previewing XGBoost

  • Wrangling data

  • Predicting regression

  • Predicting classification