Introduction to Classifier Performance Analysis with R

Author:   Sutaip L.C. Saw
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032850108


Pages:   202
Publication Date:   03 December 2024
Format:   Paperback
Availability:   In Print   Availability explained
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Introduction to Classifier Performance Analysis with R


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Overview

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.

Full Product Details

Author:   Sutaip L.C. Saw
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.453kg
ISBN:  

9781032850108


ISBN 10:   1032850108
Pages:   202
Publication Date:   03 December 2024
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Chapter 01 Introduction to Classification Chapter 02 Classifier Performance Measures Chapter 03 Classifier Performance Curves Chapter 04 Comparative Analysis of Classifiers Chapter 05 Multiclass CPA Chapter 06 Additional Topics in CPA Appendix Bibliography Index

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Author Information

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.

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