Breiman's "Two Cultures" Revisited and Reconciled

• URL: http://arxiv.org/abs/2005.13596v1
• Date: Wed, 27 May 2020 19:02:56 GMT
• Title: Breiman's "Two Cultures" Revisited and Reconciled
• Authors: Subhadeep (DEEP) Mukhopadhyay and Kaijun Wang
• Abstract summary: Two cultures of data modeling: parametric statistical and algorithmic machine learning. The widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a landmark paper published in 2001, Leo Breiman described the tense standoff between two cultures of data modeling: parametric statistical and algorithmic machine learning. The cultural division between these two statistical learning frameworks has been growing at a steady pace in recent years. What is the way forward? It has become blatantly obvious that this widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole. This article presents a solution by establishing a link between the two cultures. Through examples, we describe the challenges and potential gains of this new integrated statistical thinking.

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