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 2: Decision Trees in Depth

In this chapter, you will gain proficiency with decision trees, the primary machine learning algorithm from which XGBoost models are built. You will also gain first-hand experience in the science and art of hyperparameter fine-tuning. Since decision trees are the foundation of XGBoost models, the skills that you learn in this chapter are essential to building robust XGBoost models going forward.

In this chapter, you will build and evaluate decision tree classifiers and decision tree regressors, visualize and analyze decision trees in terms of variance and bias, and fine-tune decision tree hyperparameters. In addition, you will apply decision trees to a case study that predicts heart disease in patients.

This chapter covers the following main topics:

  • Introducing decision trees with XGBoost

  • Exploring decision trees

  • Contrasting variance and bias

  • Tuning decision tree hyperparameters

  • Predicting heart disease – a case...