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 learned the difference between bagging and boosting. You learned how gradient boosting works by building a gradient boosting regressor from scratch. You implemented a variety of gradient boosting hyperparameters, including learning_rate, n_estimators, max_depth, and subsample, which results in stochastic gradient boosting. Finally, you used big data to predict whether stars have exoplanets by comparing the times of GradientBoostingClassifier and XGBoostClassifier, with XGBoostClassifier emerging as twice to over ten times as fast and more accurate.

The advantage of learning these skills is that you now understand when to apply XGBoost rather than similar machine learning algorithms such as gradient boosting. You can now build stronger XGBoost and gradient boosting models by properly taking advantage of core hyperparameters, including n_estimators and learning_rate. Furthermore, you have developed the capacity to time all computations instead of relying...