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

Stacking models

"For stacking and boosting I use xgboost, again primarily due to familiarity and its proven results."

David Austin, Kaggle Winner


In this final section, we will examine one of the most powerful tricks frequently used by Kaggle winners, called stacking.

What is stacking?

Stacking combines machine learning models at two different levels: the base level, whose models make predictions on all the data, and the meta level, which takes the predictions of the base models as input and uses them to generate final predictions.

In other words, the final model in stacking does not take the original data as input, but rather takes the predictions of the base machine learning models as input.

Stacked models have found huge success in Kaggle competitions. Most Kaggle competitions have merger deadlines, where individuals and teams can...