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

Analyzing XGBoost parameters

In this section, we will analyze the parameters that XGBoost uses to create state-of-the-art machine learning models with a mathematical derivation.

We will maintain the distinction between parameters and hyperparameters as presented in Chapter 2, Decision Trees in Depth. Hyperparameters are chosen before the model is trained, whereas parameters are chosen while the model is being trained. In other words, the parameters are what the model learns from the data.

The derivation that follows is taken from the XGBoost official documentation, Introduction to Boosted Trees, at

Learning objective

The learning objective of a machine learning model determines how well the model fits the data. In the case of XGBoost, the learning objective consists of two parts: the loss function and the regularization term.

Mathematically, XGBoost's learning objective may be defined as follows: