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

Bagging ensembles

In this section, you will learn why ensemble methods are usually superior to individual machine learning models. Furthermore, you will learn about the technique of bagging. Both are essential features of random forests.

Ensemble methods

In machine learning, an ensemble method is a machine learning model that aggregates the predictions of individual models. Since ensemble methods combine the results of multiple models, they are less prone to error, and therefore tend to perform better.

Imagine your goal is to determine whether a house will sell within the first month of being on the market. You run several machine learning algorithms and find that logistic regression gives 80% accuracy, decision trees 75% accuracy, and k-nearest neighbors 77% accuracy.

One option is to use logistic regression, the most accurate model, as your final model. A more compelling option is to combine the predictions of each individual model.

For classifiers, the standard option...