Hands-On Gradient Boosting with XGBoost and scikit-learn PDF

Hands-On Gradient Boosting with XGBoost and scikit-learn PDF
Hands-On Gradient Boosting with XGBoost and scikit-learn PDF Github
eBook: Hands-On Gradient Boosting with XGBoost and scikit-learn PDF : Perform accessible machine learning and extreme gradient boosting with Python PDF by Corey Wade and Kevin Glynn

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The Hands-On Gradient Boosting with XGBoost and scikit-learn eBook 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.

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July 25, 2021
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