# The Representation of Meaningful Precision, and Accuracy

arXiv: https://arxiv.org/abs/2410.14721 · HTML: https://mlsanity.com/paper/2410.14721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice](https://mlsanity.com/paper/2404.16958/) (2024)
- [General Rough Modeling of Cluster Analysis](https://mlsanity.com/paper/2106.04683/) (2021)
- [Rough Randomness and its Application](https://mlsanity.com/paper/2304.00005/) (2023)
- [Algebraic Models for Qualified Aggregation in General Rough Sets, and Reasoning Bias Discovery](https://mlsanity.com/paper/2309.03217/) (2023)

## Abstract (arXiv)

The concepts of precision, and accuracy are domain and problem dependent. The simplified numeric hard and soft measures used in the fields of statistical learning, many types of machine learning, and binary or multiclass classification problems are known to be of limited use for understanding the meaningfulness of models or their relevance. Arguably, they are neither of patterns nor proofs. Further, there are no good measures or representations for analogous concepts in the cognition domain. In this research, the key issues are reflected upon, and a compositional knowledge representation approach in a minimalist general rough framework is proposed for the problem contexts. The latter is general enough to cover most application contexts, and may be applicable in the light of improved computational tools available.