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)
1
Section 1: Bagging and Boosting
6
Section 2: XGBoost
10
Section 3: Advanced XGBoost

Chapter 5: XGBoost Unveiled

In this chapter, you will finally see Extreme Gradient Boosting, or XGBoost, as it is. XGBoost is presented in the context of the machine learning narrative that we have built up, from decision trees to gradient boosting. The first half of the chapter focuses on the theory behind the distinct advancements that XGBoost brings to tree ensemble algorithms. The second half focuses on building XGBoost models within the Higgs Boson Kaggle Competition, which unveiled XGBoost to the world.

Specifically, you will identify speed enhancements that make XGBoost faster, discover how XGBoost handles missing values, and learn the mathematical derivation behind XGBoost's regularized parameter selection. You will establish model templates for building XGBoost classifiers and regressors. Finally, you will look at the Large Hadron Collider, where the Higgs boson was discovered, where you will weigh data and make predictions using the original XGBoost Python API.

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