Breiman's "Two Cultures" Revisited and Reconciled

05/27/2020
by   Subhadeep, et al.
8

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/23/2021

Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning

In 2001, Leo Breiman wrote of a divide between "data modeling" and "algo...
research
07/06/2022

The "Collections as ML Data" Checklist for Machine Learning Cultural Heritage

Within the cultural heritage sector, there has been a growing and concer...
research
08/03/2022

Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles

Integrating sensing and communication is a defining theme for future wir...
research
02/08/2023

Potential Outcome and Decision Theoretic Foundations for Statistical Causality

In a recent paper published in the Journal of Causal Inference, Philip D...
research
10/24/2022

Bridging Machine Learning and Sciences: Opportunities and Challenges

The application of machine learning in sciences has seen exciting advanc...
research
04/14/2021

Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms

We consider an extension of Leo Breiman's thesis from "Statistical Model...
research
06/10/2023

Practical Problems of Statistical Learning

Statistical models have seen a significant rise in popularity in recent ...

Please sign up or login with your details

Forgot password? Click here to reset