在构建评估模型时应该优化什么?

C. Lokan
{"title":"在构建评估模型时应该优化什么?","authors":"C. Lokan","doi":"10.1109/METRICS.2005.55","DOIUrl":null,"url":null,"abstract":"When estimation models are derived from existing data, they are commonly evaluated using statistics such as mean magnitude of relative error. But when the models are derived in the first place, it is usually by optimizing something else - typically, as in statistical regression, by minimizing the sum of squared deviations. How do estimation models for typical software engineering data fare, on various common accuracy statistics, if they are derived using other \"fitness functions\"? In this study, estimation models are built using a variety of fitness functions, and evaluated using a wide range of accuracy statistics. We find that models based on minimizing actual errors generally out-perform models based on minimizing relative errors. Given the nature of software engineering data sets, minimizing the sum of absolute deviations seems an effective compromise","PeriodicalId":402415,"journal":{"name":"11th IEEE International Software Metrics Symposium (METRICS'05)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":"{\"title\":\"What should you optimize when building an estimation model?\",\"authors\":\"C. Lokan\",\"doi\":\"10.1109/METRICS.2005.55\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When estimation models are derived from existing data, they are commonly evaluated using statistics such as mean magnitude of relative error. But when the models are derived in the first place, it is usually by optimizing something else - typically, as in statistical regression, by minimizing the sum of squared deviations. How do estimation models for typical software engineering data fare, on various common accuracy statistics, if they are derived using other \\\"fitness functions\\\"? In this study, estimation models are built using a variety of fitness functions, and evaluated using a wide range of accuracy statistics. We find that models based on minimizing actual errors generally out-perform models based on minimizing relative errors. Given the nature of software engineering data sets, minimizing the sum of absolute deviations seems an effective compromise\",\"PeriodicalId\":402415,\"journal\":{\"name\":\"11th IEEE International Software Metrics Symposium (METRICS'05)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"11th IEEE International Software Metrics Symposium (METRICS'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/METRICS.2005.55\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"11th IEEE International Software Metrics Symposium (METRICS'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/METRICS.2005.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 35

摘要

当估计模型是从现有数据中导出时,通常使用统计数据(如相对误差的平均幅度)对其进行评估。但是,当模型首先被导出时,通常是通过优化其他东西-通常,如在统计回归中,通过最小化平方偏差的总和。如果典型软件工程数据的估计模型是使用其他“适应度函数”推导出来的,那么在各种常见的精度统计数据上,它们的表现如何?在本研究中,使用各种适应度函数建立估计模型,并使用广泛的精度统计进行评估。我们发现基于最小化实际误差的模型通常优于基于最小化相对误差的模型。考虑到软件工程数据集的性质,最小化绝对偏差的总和似乎是一种有效的妥协
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What should you optimize when building an estimation model?
When estimation models are derived from existing data, they are commonly evaluated using statistics such as mean magnitude of relative error. But when the models are derived in the first place, it is usually by optimizing something else - typically, as in statistical regression, by minimizing the sum of squared deviations. How do estimation models for typical software engineering data fare, on various common accuracy statistics, if they are derived using other "fitness functions"? In this study, estimation models are built using a variety of fitness functions, and evaluated using a wide range of accuracy statistics. We find that models based on minimizing actual errors generally out-perform models based on minimizing relative errors. Given the nature of software engineering data sets, minimizing the sum of absolute deviations seems an effective compromise
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信