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

Summary

In this chapter, you prepared for hyperparameter fine-tuning by establishing a baseline XGBoost model using StratifiedKFold. Then, you combined GridSearchCV and RandomizedSearchCV to form one powerful function. You learned the standard definitions, ranges, and applications of key XGBoost hyperparameters, in addition to a new technique called early stopping. You synthesized all functions, hyperparameters, and techniques to fine-tune the heart disease dataset, gaining an impressive five percentage points from the default XGBoost classifier.

XGBoost hyperparameter fine-tuning takes time to master, and you are well on your way. Fine-tuning hyperparameters is a key skill that separates machine learning experts from machine learning novices. Knowledge of XGBoost hyperparameters is not just useful, it's essential to get the most out of the machine learning models that you build.

Congratulations on completing this important chapter.

Next, we present a case study of XGBoost...