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

How gradient boosting works

In this section, we will look under the hood of gradient boosting and build a gradient boosting model from scratch by training new trees on the errors of the previous trees. The key mathematical idea here is the residual. Next, we will obtain the same results using scikit-learn's gradient boosting algorithm.


The residuals are the difference between the errors and the predictions of a given model. In statistics, residuals are commonly analyzed to determine how good a given linear regression model fits the data.

Consider the following examples:

  1. Bike rentals

    a) Prediction: 759

    b) Result: 799

    c) Residual: 799 - 759 = 40

  2. Income

    a) Prediction: 100,000

    b) Result: 88,000

    c) Residual: 88,000 –100,000 = -12,000

As you can see, residuals tell you how far the model's predictions are from reality, and they may be positive or negative.

Here is a visual example displaying the residuals of a linear regression line: