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

Predicting heart disease – a case study

You have been asked by a hospital to use machine learning to predict heart disease. Your job is to develop a model and highlight two to three important features that doctors and nurses can focus on to improve patient health.

You decide to use a decision tree classifier with fine-tuned hyperparameters. After the model has been built, you will interpret results using feature_importances_, an attribute that determines the most important features in predicting heart disease.

Heart Disease dataset

The Heart Disease dataset has been uploaded to GitHub as heart_disease.csv. This is a slight modification to the original Heart Disease dataset ( provided by the UCI Machine Learning Repository ( with null values cleaned up for your convenience.

Upload the file and display the first five rows as follows:

df_heart ...