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: Hardback
Length: 340 pages
Publication date: 18 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 key 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 encompass complex operations such as pre-processing, modelling, 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 coverage 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 new to the field as well as seasoned practitioners.

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. These code snippets are written in a convenient and expressive manner, making it easy to understand and implement machine learning techniques in R.

Convenient and Expressive Machine Learning Pipelining: The book emphasizes the power of machine learning pipelining, enabling advanced modelling and analysis. It covers topics that are often overlooked in other machine learning books, providing readers with a comprehensive understanding of the entire machine learning workflow.

Target Audience: The book is primarily aimed at researchers, practitioners, and graduate students who utilize machine learning or are interested in exploring its potential. 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 must-read for anyone seeking to unlock the full potential of machine learning in R. With its comprehensive coverage, ready-to-use code samples, and expressive machine learning pipelining, this book provides a valuable resource for researchers, practitioners, and students alike. Whether you are a beginner or an experienced machine learning practitioner, this book will help you advance your skills and knowledge in this rapidly evolving field.

Weight: 820g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032515670