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This study extends statistical modeling's two cultures to algorithmic approaches. It explores parametric regressions, interpretable algorithms, and complex, explainable algorithms for broader modeling understanding.

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Area of Science:

  • Statistics
  • Machine Learning
  • Computer Science

Background:

  • Leo Breiman's "Statistical Modeling: The Two Cultures" proposed two distinct approaches to statistical modeling.
  • Algorithmic modeling offers a powerful alternative and complement to traditional statistical methods.

Purpose of the Study:

  • To extend Breiman's "Two Cultures" framework to encompass algorithmic modeling.
  • To categorize algorithmic modeling approaches, including parametric regressions, interpretable algorithms, and complex (potentially explainable) algorithms.

Main Methods:

  • Conceptual extension of Breiman's thesis.
  • Analysis of algorithmic modeling paradigms.
  • Categorization based on model complexity and interpretability.

Main Results:

  • A proposed bifurcation within algorithmic modeling.
  • Identification of key characteristics for parametric regressions, interpretable algorithms, and complex algorithms.
  • Framework for understanding diverse algorithmic modeling strategies.

Conclusions:

  • Algorithmic modeling represents a significant and distinct paradigm in statistical science.
  • The proposed extension provides a richer understanding of modern modeling techniques.
  • Further research can explore the implications of this bifurcation for model selection and evaluation.