# On the Value of ML Models

arXiv: https://arxiv.org/abs/2112.06775 · HTML: https://mlsanity.com/paper/2112.06775/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)

## Built on by (most cited first, 3 of 3)

- [Rethinking and Recomputing the Value of Machine Learning Models](https://mlsanity.com/paper/2209.15157/) (2022)
- [Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects](https://mlsanity.com/paper/2409.03805/) (2024)
- [Constraining Representations Yields Models That Know What They Don't Know](https://mlsanity.com/paper/2208.14488/) (2022)

## Abstract (arXiv)

We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications. For a specific class of use cases -- selective classification -- we show that not only can it be simple enough to do, but that it has import consequences and provides insights what to look for in a ``good'' ML model.