Applied Machine Learning Using mlr3 in R

Applied Machine Learning Using mlr3 in R provides an overview of flexible and robust machine learning methods in R, with an emphasis on implementation using the mlr3 ecosystem. It covers basic and advanced topics, including model building, hyperparameter tuning, pipeline construction, and custom extensions, making it a valuable resource for researchers, practitioners, and students in the field.

Format: Paperback / softback
Length: 340 pages
Publication date: 19 January 2024
Publisher: Taylor & Francis Ltd

mlr3 is an award-winning ecosystem of R packages that have been meticulously crafted to unlock state-of-the-art machine learning capabilities within the R programming language. Applied Machine Learning Using mlr3 in R provides a comprehensive and in-depth exploration of flexible and robust machine learning methodologies, with a particular focus on their implementation using the mlr3 framework in R. This book delves into various critical topics, ranging from fundamental machine learning tasks such as building and evaluating predictive models to advanced techniques such as hyperparameter tuning, building machine learning pipelines that execute complex operations such as pre-processing, modeling, and aggregation of predictions, and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.

Key Features:

Comprehensive Coverage: The book offers an extensive and detailed account of the mlr3 ecosystem, catering to users and developers alike. It provides a thorough explanation and illustration of basic and advanced machine learning concepts, ensuring a solid foundation for those seeking to delve into the field.

Ready-to-Use Code Samples: Extensive code samples are provided throughout the book, allowing users to directly adapt and utilize them for their specific applications. This hands-on approach empowers users to apply the learned concepts immediately, enhancing their understanding and productivity.

Convenient and Expressive Machine Learning Pipelining: The book emphasizes the power of machine learning pipelining, enabling advanced modeling and analysis. It offers convenient and expressive syntax, making it easy to construct and streamline machine learning workflows, enabling efficient and effective data manipulation and modeling.

Coverage of Often Ignored Topics: This book goes beyond the traditional scope of machine learning literature, addressing topics that are often overlooked or underrepresented. It provides comprehensive coverage of topics such as cross-validation, model selection, and interpretability, ensuring a well-rounded understanding of machine learning principles.

Primary Target Audience: The book is primarily aimed at researchers, practitioners, and graduate students who utilize machine learning or have an interest in exploring its applications. It serves as an excellent textbook for introductory or advanced machine learning courses that utilize R, providing a comprehensive reference for individuals working with machine learning methods and in industry for exploratory experiments in machine learning.

In summary, Applied Machine Learning Using mlr3 in R is a comprehensive and indispensable resource for anyone seeking to unlock the full potential of machine learning in R. With its in-depth coverage, ready-to-use code samples, expressive machine learning pipelining, and focus on often ignored topics, this book empowers users to build robust and effective machine learning models and advance their understanding of the field. Whether you are a researcher, practitioner, or student, this book is a valuable asset for your machine learning journey.

Weight: 660g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032507545