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.
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.
This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Corey Wade, M.S. Mathematics, M.F.A. Writing and Consciousness, is the founder and director of Berkeley Coding Academy, where he teaches machine learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at the Independent Study Program of Berkeley High School, where he teaches programming and advanced math. His additional experience includes teaching natural language processing with Hello World, developing data science curricula with Pathstream, and publishing original statistics (3NG) and machine learning articles with Towards Data Science, Springboard, and Medium. Corey is co-author of the Python Workshop, also published by Packt.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
EUR 5,73 per la spedizione da Regno Unito a Italia
Destinazione, tempi e costiEUR 10,17 per la spedizione da Regno Unito a Italia
Destinazione, tempi e costiDa: Goldstone Books, Llandybie, Regno Unito
paperback. Condizione: Very Good. All orders are dispatched within one working day from our UK warehouse. We've been selling books online since 2004! We have over 750,000 books in stock. No quibble refund if not completely satisfied. Codice articolo mon0007492377
Quantità: 1 disponibili
Da: Better World Books, Mishawaka, IN, U.S.A.
Condizione: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects. Codice articolo 51831494-6
Quantità: 1 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand. Codice articolo 401730361
Quantità: 4 disponibili
Da: SecondSale, Montgomery, IL, U.S.A.
Condizione: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. Codice articolo 00082791791
Quantità: 1 disponibili
Da: dsmbooks, Liverpool, Regno Unito
paperback. Condizione: New. New. book. Codice articolo D8S0-3-M-1839218355-6
Quantità: 1 disponibili