探戈需要两个人:统计建模和机器学习

V. Kumar, M. Vannan
{"title":"探戈需要两个人:统计建模和机器学习","authors":"V. Kumar, M. Vannan","doi":"10.1080/21639159.2020.1808838","DOIUrl":null,"url":null,"abstract":"ABSTRACT Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits.","PeriodicalId":45711,"journal":{"name":"Journal of Global Scholars of Marketing Science","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/21639159.2020.1808838","citationCount":"4","resultStr":"{\"title\":\"It takes two to tango: Statistical modeling and machine learning\",\"authors\":\"V. Kumar, M. Vannan\",\"doi\":\"10.1080/21639159.2020.1808838\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits.\",\"PeriodicalId\":45711,\"journal\":{\"name\":\"Journal of Global Scholars of Marketing Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2021-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/21639159.2020.1808838\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Global Scholars of Marketing Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21639159.2020.1808838\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Global Scholars of Marketing Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21639159.2020.1808838","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 4

摘要

摘要统计方法(SM)几代人以来一直在从任何类型的数据中产生见解方面占主导地位。然而,随着技术的最新进步,机器学习(ML)已成为一种广泛使用的方法,可以更容易地生成见解。虽然统计方法的追随者对ML有不同的看法,而ML的追随者对SM有不同的观点,但本文分离了这两种方法的优点,并根据目的、上下文、使用频率、成本、专业知识和时间提出了何时使用哪种方法的论点。具体来说,SM的主要目的是推理,ML的主要目的则是预测。此外,本文更进一步,创建了一个场景,表明当我们将使用统计方法的学习结合起来并将其应用于机器学习时,最终收益可能大于每种方法的收益之和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
It takes two to tango: Statistical modeling and machine learning
ABSTRACT Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits.
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
4.00
自引率
6.20%
发文量
21
期刊最新文献
Talk to engage: The influence of smartphone voice assistants on consumers’ brand engagement Being moral motivates consumers to work harder and accept challenges The evolution of customer engagement in the digital era for business: A review and future research agenda Impact of artificial intelligence (AI) chatbot characteristics on customer experience and customer satisfaction Identifying the influence of obsolescence risk and health beliefs in fitness wearable healthcare technology
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1