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

Exploring alternative base learners

The base learner is the machine learning model that XGBoost uses to build the first model in its ensemble. The word base is used because it's the model that comes first, and the word learner is used because the model iterates upon itself after learning from the errors.

Decision trees have emerged as the preferred base learners for XGBoost on account of the excellent scores that boosted trees consistently produce. The popularity of decision trees extends beyond XGBoost to other ensemble algorithms such as random forests and extremely randomized trees, which you can preview in the scikit-learn documentation under ExtraTreesClassifier and ExtraTreesRegressor (

In XGBoost, the default base learner, known as gbtree, is one of several base learners. There is also gblinear, a gradient boosted linear model, and dart, a variation of decision trees that includes a dropout technique based on...