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

Building XGBoost models

In the first two sections, you learned how XGBoost works under the hood with parameter derivations, regularization, speed enhancements, and new features such as the missing parameter to compensate for null values.

In this book, we primarily build XGBoost models with scikit-learn. The scikit-learn XGBoost wrapper was released in 2019. Before full immersion with scikit-learn, building XGBoost models required a steeper learning curve. Converting NumPy arrays to dmatrices, for instance, was mandatory to take advantage of the XGBoost framework.

In scikit-learn, however, these conversions happen behind the scenes. Building XGBoost models in scikit-learn is very similar to building other machine learning models in scikit-learn, as you have experienced throughout this book. All standard scikit-learn methods, such as .fit, and .predict, are available, in addition to essential tools such as train_test_split, cross_val_score, GridSearchCV, and RandomizedSearchCV...