XGBoost for Regression Predictive Modeling and Time Series Analysis

eBook Details:

• Paperback: 308 pages
• Publisher: WOW! eBook (December 13, 2024)
• Language: English
• ISBN-10: 180512305X
• ISBN-13: 978-1805123057

eBook Description:

XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build, evaluate, and deploy predictive models with expert guidance. Master the art of predictive modeling with XGBoost and gain hands-on experience in building powerful regression, classification, and time series models using the XGBoost Python API.

XGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this XGBoost for Regression Predictive Modeling and Time Series Analysis 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.

• Build a strong, intuitive understanding of the XGBoost algorithm and its benefits
• Implement XGBoost using the Python API for practical applications
• Evaluate model performance using appropriate metrics
• Deploy XGBoost models into production environments
• Handle complex datasets and extract valuable insights
• Gain practical experience in feature engineering, feature selection, and categorical encoding

By the end of this XGBoost for Regression Predictive Modeling and Time Series Analysis 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.