Joyce Weiner – författare

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 APIKey FeaturesGet up and running with this quick-start guide to building a classifier using XGBoostGet an easy-to-follow, in-depth explanation of the XGBoost technical paperLeverage XGBoost for time series forecasting by using moving average, frequency, and window methodsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionXGBoost 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.What you will learnBuild a strong, intuitive understanding of the XGBoost algorithm and its benefitsImplement XGBoost using the Python API for practical applicationsEvaluate model performance using appropriate metricsDeploy XGBoost models into production environmentsHandle complex datasets and extract valuable insightsGain practical experience in feature engineering, feature selection, and categorical encodingWho this book is forThis book is for data scientists, machine learning practitioners, analysts, and professionals interested in predictive modeling and time series analysis. Basic coding knowledge and familiarity with Python, GitHub, and other DevOps tools are required.

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

Key Features

Get up and running with this quick-start guide to building a classifier using XGBoostGet an easy-to-follow, in-depth explanation of the XGBoost technical paperLeverage XGBoost for time series forecasting by using moving average, frequency, and window methodsPurchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

Build a strong, intuitive understanding of the XGBoost algorithm and its benefitsImplement XGBoost using the Python API for practical applicationsEvaluate model performance using appropriate metricsDeploy XGBoost models into production environmentsHandle complex datasets and extract valuable insightsGain practical experience in feature engineering, feature selection, and categorical encoding

Who this book is for

This book is for data scientists, machine learning practitioners, analysts, and professionals interested in predictive modeling and time series analysis. Basic coding knowledge and familiarity with Python, GitHub, and other DevOps tools are required.

Recent data shows that 87% of Artificial Intelligence/Big Data projects don’t make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.

This Second Edition addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid these pitfalls. Current statistics show that 87% of AI and Big Data projects fail by never reaching deployment, making this book an essential resource for data science and AI practitioners, as well as managers. The author illustrates the methods and tools by including real examples from her experience building and deploying data science and AI projects. This new edition builds upon the original book with revisions, updates and features a new chapter on Generative AI.