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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. This book is for those who want a reasonably complete (at least at an introductory level) and up-to-date coverage on the analysis of classification algorithms through the use of performance measures and curves. It attempts to synthesize useful material from the vast published literature on the subject. Another motivation for the book is to show how R can be used to perform the required analysis. As computational software, R has already demonstrated its excellence to a large international community of users. Its appeal is further enhanced by recently developed packages and meta-packages for Data Science, Machine Learning, and classification performance analysis in particular. 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.
Genre: Non-Fiction > Tech & Devices
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