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

arXiv: https://arxiv.org/abs/2104.06571 · HTML: https://mlsanity.com/paper/2104.06571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Ethical Machine Learning in Health Care](https://mlsanity.com/paper/2009.10576/) (2020)
- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator](https://mlsanity.com/paper/1905.10299/) (2019)

## Abstract (arXiv)

We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions, interpretable algorithms, and complex (possibly explainable) algorithms.