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 6: XGBoost Hyperparameters

XGBoost has many hyperparameters. XGBoost base learner hyperparameters incorporate all decision tree hyperparameters as a starting point. There are gradient boosting hyperparameters, since XGBoost is an enhanced version of gradient boosting. Hyperparameters unique to XGBoost are designed to improve upon accuracy and speed. However, trying to tackle all XGBoost hyperparameters at once can be dizzying.

In Chapter 2, Decision Trees in Depth, we reviewed and applied base learner hyperparameters such as max_depth, while in Chapter 4, From Gradient Boosting to XGBoost, we applied important XGBoost hyperparameters, including n_estimators and learning_rate. We will revisit these hyperparameters in this chapter in the context of XGBoost. Additionally, we will also learn about novel XGBoost hyperparameters such as gamma and a technique called early stopping.

In this chapter, to gain proficiency in fine-tuning XGBoost hyperparameters, we will cover the...