R语言中分类器性能分析介绍

English | 2025 | ISBN: 9781032855622 | 222 pages | True PDF EPUB | 8.63 MB

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.

分类问题在商业、医学、科学、工程和其他经济领域中很常见。数据科学家和机器学习专业人士通过使用分类器来解决这些问题。选择一种对特定问题的常用数据驱动型分类算法是一项具有挑战性的任务。这个任务中一个重要方面就是分类器性能分析（CPA）。《用R进行分类器性能分析》提供了一个关于二元和多类问题中常用的CPA技术的基本介绍，以及使用R软件系统来进行分析的方法。内容涉及有关该主题的广泛文献，包括描述性和推论性的CPA方法。每章末尾都包含练习题以强化学习效果。关键特点：提供了关于二元和多类分类问题的基本介绍，包括基于统计、机器和集成学习的一些分类器。本教材覆盖了常用的二元和多类CPA技术，并从一些不那么显眼但有用的角度来看待它们。还包括一些在传统的CPA教材中没有得到足够关注的题目。强调了一些常用性能度量的局限性。还包括其性能参数以及相关的推论方法。还涵盖了对竞争分类器进行比较分析的技术。一个关键贡献在于，使用像tidyverse和tidymodels这样的R元包来进行CPA分析，特别是非常有用的yardstick包。本书对数据科学、机器学习等相关领域的大一高年级本科生和硕士研究生来说是一个有用的学习资源。感兴趣的从业者也可以从这本书中学到如何使用R来评估分类器的性能。书中提供的内容和参考文献也可用于CPA研究者的需要。

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