Reasoning Using Data: Two Old Ways and One New

M. Baiocchi, J. Rodu
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引用次数: 2

Abstract

Abstract:Instead of two cultures, the story of the last couple decades of data science is about the interplay between three different types of reasoning using data. Two of these types of reasoning were well known when Breiman wrote his Two Cultures paper – warranted reasoning (e.g., randomized trials and sampling) and model reasoning (e.g., linear models). Breiman, though he does not appear to have realized it fully, was in fact describing the dynamics arising in a data community that was making progress using the newest, third type of reasoning – outcome reasoning. In this commentary we clarify this dynamic a bit, and suggest some useful language for identifying and differentiating types of problems better suited for outcome reasoning.
数据推理:两种旧方法和一种新方法
摘要:过去几十年的数据科学故事讲述的不是两种文化,而是三种不同类型的使用数据的推理之间的相互作用。当Breiman写他的《两种文化》论文时,其中两种类型的推理是众所周知的——保证推理(如随机试验和抽样)和模型推理(如线性模型)。尽管Breiman似乎没有完全意识到这一点,但事实上,他描述的是数据社区中出现的动态,该社区正在使用最新的第三种推理——结果推理——取得进展。在这篇评论中,我们稍微澄清了这一动态,并提出了一些有用的语言来识别和区分更适合结果推理的问题类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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