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

Tuning XGBoost hyperparameters

There are many XGBoost hyperparameters, some of which have been introduced in previous chapters. The following table summarizes key XGBoost hyperparameters, most of which we cover in this book.

Note

The XGBoost hyperparameters presented here are not meant to be exhaustive, but they are meant to be comprehensive. For a complete list of hyperparameters, read the official documentation, XGBoost Parameters, at https://xgboost.readthedocs.io/en/latest/parameter.html.

Following the table, further explanations and examples are provided:

Figure 6.2 – XGBoost hyperparameter table

Figure 6.2 – XGBoost hyperparameter table

Now that the key XGBoost hyperparameters have been presented, let's get to know them better by tuning them one at a time.

Applying XGBoost hyperparameters

The XGBoost hyperparameters presented in this section are frequently fine-tuned by machine learning practitioners. After a brief explanation of each hyperparameter, we will test...