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


Congratulations on making it to the end of the book! This has been an extraordinary journey that began with basic machine learning and pandas and ended with building your own customized transformers, pipelines, and functions to deploy robust, fine-tuned XGBoost models in industry scenarios with sparse matrices to make predictions on new data.

Along the way, you have learned the story of XGBoost, from the first decision trees through random forests and gradient boosting, before discovering the mathematical details and sophistication that has made XGBoost so special. You saw time and time again that XGBoost outperforms other machine learning algorithms, and you gained essential practice in tuning XGBoost's wide-ranging hyperparameters, including n_estimators, max_depth, gamma, colsample_bylevel, missing, and scale_pos_weight.

You learned how physicists and astronomers obtained knowledge about our universe in historically important case studies, and you learned about...