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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) Język publikacji: angielski
    Hands-On Gradient Boosting with XGBoost and scikit-learn. Perform accessible machine learning and extreme gradient boosting with Python Corey Wade, Kevin Glynn - okładka ebooka

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

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

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

    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    310
    Dostępne formaty:
    PDF
    ePub
    Mobi

    Ebook

    159,00 zł

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    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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