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

Building non-correlated ensembles

"In our final model, we had XGBoost as an ensemble model, which included 20 XGBoost models, 5 random forests, 6 randomized decision tree models, 3 regularized greedy forests, 3 logistic regression models, 5 ANN models, 3 elastic net models and 1 SVM model."

Song, Kaggle Winner


The winning models of Kaggle competitions are rarely individual models; they are almost always ensembles. By ensembles, I do not mean boosting or bagging models, such as random forests or XGBoost, but pure ensembles that include any distinct models, including XGBoost, random forests, and others.

In this section, we will combine machine learning models into non-correlated ensembles to gain accuracy and reduce overfitting.

Range of models

The Wisconsin Breast Cancer dataset, used to predict whether a patient has breast cancer, has 569 rows and 30 columns, and can be viewed at https://scikit...