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    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Corey Wade, Kevin Glynn
    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python Corey Wade, Kevin Glynn - okladka książki

    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python Corey Wade, Kevin Glynn - okladka książki

    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python Corey Wade, Kevin Glynn - audiobook MP3

    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python Corey Wade, Kevin Glynn - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    310
    Dostępne formaty:
         PDF
         ePub
         Mobi
    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.

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    O autorze książki

    Corey Wade, M.S. Mathematics, M.F.A. Writing & 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 Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.

    Packt Publishing - inne książki

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