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    XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari

    (ebook) (audiobook) (audiobook) Książka w języku 1
    XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari - okladka książki

    XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari - okladka książki

    XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari - audiobook MP3

    XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari - audiobook CD

    Autorzy:
    Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    308
    Dostępne formaty:
         PDF
         ePub
    XGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this book, the authors draw on their combined experience of 40+ years in the semiconductor industry to help you harness the full potential of XGBoost, from understanding its core concepts to implementing real-world applications.
    As you progress, you'll get to grips with the XGBoost algorithm, including its mathematical underpinnings and its advantages over other ensemble methods. You'll learn when to choose XGBoost over other predictive modeling techniques, and get hands-on guidance on implementing XGBoost using both the Python API and scikit-learn API. You'll also get to grips with essential techniques for time series data, including feature engineering, handling lag features, encoding techniques, and evaluating model performance. A unique aspect of this book is the chapter on model interpretability, where you'll use tools such as SHAP, LIME, ELI5, and Partial Dependence Plots (PDP) to understand your XGBoost models. Throughout the book, you’ll work through several hands-on exercises and real-world datasets.
    By the end of this book, you'll not only be building accurate models but will also be able to deploy and maintain them effectively, ensuring your solutions deliver real-world impact.

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    O autorze książki

    Joyce Weiner has over 25 years' experience in the semiconductor industry both in manufacturing and in product engineering. Her area of technical expertise is data science and using data to drive efficiency. She has presented technical papers at INFORMS Analytics and ASME InterPack and has been a Track chair for the ASME InterPack conference and others. She holds one US patent. Her book, “Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls” was published in 2021.

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