It takes two to tango: Statistical modeling and machine learning

V. Kumar, M. Vannan
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引用次数: 4

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.
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探戈需要两个人:统计建模和机器学习
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。
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来源期刊
CiteScore
4.00
自引率
6.20%
发文量
21
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