Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)

• URL: http://arxiv.org/abs/2103.11327v1
• Date: Sun, 21 Mar 2021 07:40:37 GMT
• Title: Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)
• Authors: Jelena Bradic and Yinchu Zhu
• Abstract summary: Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world. A new frontier has emerged; the one where the role, impact, or stability of the it learning algorithms is no longer measured by prediction quality, but by inferential one.
• Score: 1.2183405753834562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward. Statistics community responded with slight optimism, some skepticism, and plenty of disbelief. Today, we are at the same crossroad anew. Faced with the enormous practical success of model-free, deep, and machine learning, we are naturally inclined to think that everything is resolved. A new frontier has emerged; the one where the role, impact, or stability of the {\it learning} algorithms is no longer measured by prediction quality, but an inferential one -- asking the questions of {\it why} and {\it if} can no longer be safely ignored.

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