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

Previewing XGBoost

Machine learning gained recognition with the first neural network in the 1940s, followed by the first machine learning checker champion in the 1950s. After some quiet decades, the field of machine learning took off when Deep Blue famously beat world chess champion Gary Kasparov in the 1990s. With a surge in computational power, the 1990s and early 2000s produced a plethora of academic papers revealing new machine learning algorithms such as random forests and AdaBoost.

The general idea behind boosting is to transform weak learners into strong learners by iteratively improving upon errors. The key idea behind gradient boosting is to use gradient descent to minimize the errors of the residuals. This evolutionary strand, from standard machine learning algorithms to gradient boosting, is the focus of the first four chapters of this book.

XGBoost is short for Extreme Gradient Boosting. The Extreme part refers to pushing the limits of computation to achieve gains in accuracy and speed. XGBoost's surging popularity is largely due to its unparalleled success in Kaggle competitions. In Kaggle competitions, competitors build machine learning models in attempts to make the best predictions and win lucrative cash prizes. In comparison to other models, XGBoost has been crushing the competition.

Understanding the details of XGBoost requires understanding the landscape of machine learning within the context of gradient boosting. In order to paint a full picture, we start at the beginning, with the basics of machine learning.

What is machine learning?

Machine learning is the ability of computers to learn from data. In 2020, machine learning predicts human behavior, recommends products, identifies faces, outperforms poker professionals, discovers exoplanets, identifies diseases, operates self-driving cars, personalizes the internet, and communicates directly with humans. Machine learning is leading the artificial intelligence revolution and affecting the bottom line of nearly every major corporation.

In practice, machine learning means implementing computer algorithms whose weights are adjusted when new data comes in. Machine learning algorithms learn from datasets to make predictions about species classification, the stock market, company profits, human decisions, subatomic particles, optimal traffic routes, and more.

Machine learning is the best tool at our disposal for transforming big data into accurate, actionable predictions. Machine learning, however, does not occur in a vacuum. Machine learning requires rows and columns of data.