Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA).

Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.

Key Features:

• An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learning.
• Commonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPA.
• Limitations of some commonly used performance measures are highlighted.
• Coverage includes performance parameters and inferential techniques for them.
• Also covered are techniques for comparative analysis of competing classifiers.
• A key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick package.

This is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Sutaip L. C. Saw holds a PhD from the The Wharton School, University of Pennsylvania. Prior to earning his PhD, he served as a statistician in the public sector. His subsequent career was spent as an academic with research interests and publications in engineering statistics and statistical computing, and he has significant teaching experience in statistical/mathematical subjects at undergraduate and postgraduate levels. Since leaving academia, he has been focused on applications of R to data mining and machine learning problems. Although his interest in classification problems and performance analysis of classifiers started while he was still an academic, it has intensified in recent years and this book is the result of time spent on the topic at hand.