Hands-On Gradient Boosting with XGBoost and scikit-learn

Hands-On Gradient Boosting with XGBoost and scikit-learn

eBook Details:

  • Paperback: 310 pages
  • Publisher: WOW! eBook (November 10, 2020)
  • Language:¬†English
  • ISBN-10: 1839218355
  • ISBN-13: 978-1839218354

eBook Description:

Hands-On Gradient Boosting with XGBoost and scikit-learn: Get to grips with building robust XGBoost models using Python and scikit-learn for deployment

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.

  • Build gradient boosting models from scratch
  • Develop XGBoost regressors and classifiers with accuracy and speed
  • Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters
  • Automatically correct missing values and scale imbalanced data
  • Apply alternative base learners like dart, linear models, and XGBoost random forests
  • Customize transformers and pipelines to deploy XGBoost models
  • Build non-correlated ensembles and stack XGBoost models to increase accuracy

By the end of the Hands-On Gradient Boosting with XGBoost and scikit-learn book, you’ll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.

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