教育和认证考试中贝叶斯和逻辑回归子尺度概率的准确性。

Q2 Social Sciences
Lawrence M. Rudner
{"title":"教育和认证考试中贝叶斯和逻辑回归子尺度概率的准确性。","authors":"Lawrence M. Rudner","doi":"10.7275/Q7ZZ-D655","DOIUrl":null,"url":null,"abstract":"In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows that the conclusion also applies to the probabilities estimated from short subtests of mental abilities and that small samples can yield excellent accuracy. The calculated Bayes probabilities can be used to provide meaningful examinee feedback regardless of whether the test was originally designed to be unidimensional.","PeriodicalId":20361,"journal":{"name":"Practical Assessment, Research and Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests.\",\"authors\":\"Lawrence M. Rudner\",\"doi\":\"10.7275/Q7ZZ-D655\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows that the conclusion also applies to the probabilities estimated from short subtests of mental abilities and that small samples can yield excellent accuracy. The calculated Bayes probabilities can be used to provide meaningful examinee feedback regardless of whether the test was originally designed to be unidimensional.\",\"PeriodicalId\":20361,\"journal\":{\"name\":\"Practical Assessment, Research and Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Practical Assessment, Research and Evaluation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7275/Q7ZZ-D655\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Practical Assessment, Research and Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7275/Q7ZZ-D655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 4

摘要

在机器学习文献中,人们普遍认为,随着校准样本量的增加,Naïve贝叶斯分类器在分类精度方面最初优于逻辑回归分类器。应用于在线期末考试和高度重视的认证考试的子测试,本研究表明,结论也适用于从心理能力的短子测试估计的概率,并且小样本可以产生极好的准确性。计算出的贝叶斯概率可以用来提供有意义的考生反馈,而不管测试最初是否被设计为一维的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests.
In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows that the conclusion also applies to the probabilities estimated from short subtests of mental abilities and that small samples can yield excellent accuracy. The calculated Bayes probabilities can be used to provide meaningful examinee feedback regardless of whether the test was originally designed to be unidimensional.
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
CiteScore
2.60
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:481959085
Book学术官方微信