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

From bagging to boosting

In Chapter 3, Bagging with Random Forests, you learned why ensemble machine learning algorithms such as random forests make better predictions by combining many machine learning models into one. Random forests are classified as bagging algorithms because they take the aggregates of bootstrapped samples (decision trees).

Boosting, by contrast, learns from the mistakes of individual trees. The general idea is to adjust new trees based on the errors of previous trees.

In boosting, correcting errors for each new tree is a distinct approach from bagging. In a bagging model, new trees pay no attention to previous trees. Also, new trees are built from scratch using bootstrapping, and the final model aggregates all individual trees. In boosting, however, each new tree is built from the previous tree. The trees do not operate in isolation; instead, they are built on top of one another.

Introducing AdaBoost

AdaBoost is one of the earliest and most popular...