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

Introducing decision trees with XGBoost

XGBoost is an ensemble method, meaning that it is composed of different machine learning models that combine to work together. The individual models that make up the ensemble in XGBoost are called base learners.

Decision trees, the most commonly used XGBoost base learners, are unique in the machine learning landscape. Instead of multiplying column values by numeric weights, as in linear regression and logistic regression (Chapter 1, Machine Learning Landscape), decision trees split the data by asking questions about the columns. In fact, building decision trees is like playing a game of 20 Questions.

For instance, a decision tree may have a temperature column, and that column could branch into two groups, one with temperatures above 70 degrees, and one with temperatures below 70 degrees. The next split could be based on the seasons, following one branch if it's summer and another branch otherwise. Now the data has been split into four...