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

Finding the Higgs boson – case study

In this section, we will review the Higgs Boson Kaggle Competition, which brought XGBoost into the machine learning spotlight. In order to set the stage, the historical background is given before moving on to model development. The models that we build include a default model provided by XGBoost at the time of the competition and a reference to the winning solution provided by Gabor Melis. Kaggle accounts are not required for this text, so we will not take the time to show you how to make submissions. We have provided guidelines if you are interested.

Physics background

In popular culture, the Higgs boson is known as the God particle. Theorized by Peter Higgs in 1964, the Higgs boson was introduced to explain why particles have mass.

The search to find the Higgs boson culminated in its discovery in 2012 in the Large Hadron Collider at CERN (Geneva, Switzerland). Nobel Prizes were awarded and the Standard Model of physics, the model...