Introduction to Classifier Performance Analysis with R
Saw, Sutaip L. C.
- 出版商: CRC
- 出版日期: 2024-12-03
- 售價: $2,440
- 貴賓價: 9.5 折 $2,318
- 語言: 英文
- 頁數: 202
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032850108
- ISBN-13: 9781032850108
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相關主題
商品描述
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.
商品描述(中文翻譯)
分類問題在商業、醫學、科學、工程及其他經濟領域中非常普遍。資料科學家和機器學習專業人士透過使用分類器來解決這些問題。為特定問題選擇合適的數據驅動分類演算法是一項具有挑戰性的任務。這項任務中一個重要的方面是分類器性能分析(CPA)。
《使用 R 進行分類器性能分析入門》提供了對於二元和多類問題常用 CPA 技術的介紹,以及使用 R 軟體系統來完成分析的方式。內容涵蓋了該主題上豐富的文獻,包括對 CPA 的描述性和推論性方法。每章結尾都包含練習題以加強學習。
主要特色:
- 提供二元和多類分類問題的介紹,包括一些基於統計、機器學習和集成學習的分類器。
- 涵蓋了二元和多類 CPA 的常用技術,其中一些來自不太知名但有用的觀點。內容還包括在教科書中未受到太多關注的重要主題。
- 突出了一些常用性能指標的限制。
- 涵蓋了性能參數及其推論技術。
- 也包括了對競爭分類器的比較分析技術。
- 一個重要的貢獻是使用關鍵的 R 元包,如 tidyverse 和 tidymodels 進行 CPA,特別是非常有用的 yardstick 套件。
這是一本對於資料科學、機器學習及相關學科的高年級本科生和碩士生非常有用的資源。對於有興趣學習如何使用 R 來評估分類器性能的實務工作者來說,這本書也可能帶來益處。書中的材料和參考文獻也能滿足 CPA 研究人員的需求。
作者簡介
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.
作者簡介(中文翻譯)
Sutaip L. C. Saw 擁有賓夕法尼亞大學沃頓商學院的博士學位。在獲得博士學位之前,他曾在公共部門擔任統計師。他隨後的職業生涯主要是在學術界,研究興趣和出版物集中於工程統計和統計計算,並且在本科和研究生層級的統計/數學科目上擁有豐富的教學經驗。自離開學術界以來,他專注於將 R 應用於資料挖掘和機器學習問題。儘管他對分類問題和分類器性能分析的興趣始於學術生涯,但近年來這一興趣有所加深,而本書正是他在該主題上投入時間的結果。