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Hands-On Gradient Boosting with XGBoost and scikit-learn

Hands-On Gradient Boosting with XGBoost and scikit-learn

By : Wade
4.7 (7)
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Hands-On Gradient Boosting with XGBoost and scikit-learn

Hands-On Gradient Boosting with XGBoost and scikit-learn

4.7 (7)
By: 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)
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1
Section 1: Bagging and Boosting
6
Section 2: XGBoost
10
Section 3: Advanced XGBoost

Designing XGBoost

XGBoost is a significant upgrade from gradient boosting. In this section, you will identify the key features of XGBoost that distinguish it from gradient boosting and other tree ensemble algorithms.

Historical narrative

With the acceleration of big data, the quest to find awesome machine learning algorithms to produce accurate, optimal predictions began. Decision trees produced machine learning models that were too accurate and failed to generalize well to new data. Ensemble methods proved more effective by combining many decision trees via bagging and boosting. A leading algorithm that emerged from the tree ensemble trajectory was gradient boosting.

The consistency, power, and outstanding results of gradient boosting convinced Tianqi Chen from the University of Washington to enhance its capabilities. He called the new algorithm XGBoost, short for Extreme Gradient Boosting. Chen's new form of gradient boosting included built-in regularization and impressive...

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