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

Chapter 7: Discovering Exoplanets with XGBoost

In this chapter, you will journey through the stars in an attempt to discover exoplanets with XGBClassifier as your guide.

The reason for this chapter is twofold. The first is that it's important to gain practice in a top-to-bottom study using XGBoost since for all practical purposes, that is what you will normally do with XGBoost. Although you may not discover exoplanets with XGBoost on your own, the strategies that you implement here, which include choosing the correct scoring metric and carefully fine-tuning hyperparameters with that scoring metric in mind, apply to any practical use of XGBoost. The second reason for this particular case study is that it's essential for all machine learning practitioners to be proficient at competently handling imbalanced datasets, which is the key theme of this particular chapter.

Specifically, you will gain new skills in using the confusion matrix and the classification report, understanding...