Breiman's "Two Cultures" Revisited and Reconciled
arXiv:2005.13596
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.
arXiv commentThis paper celebrates the 70th anniversary of Statistical Machine Learning--- how far we've come, and how far we have to go. Keywords: Integrated statistical learning theory, Exploratory machine learning, Uncertainty prediction machine, ML-powered modern applied statistics, Information theory