# Absolute Evaluation Measures for Machine Learning: A Survey

arXiv: https://arxiv.org/abs/2507.03392 · HTML: https://mlsanity.com/paper/2507.03392/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 76 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [A Survey of Evaluation Metrics Used for NLG Systems](https://mlsanity.com/paper/2008.12009/) (2020)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [On Evaluation Metrics for Graph Generative Models](https://mlsanity.com/paper/2201.09871/) (2022)
- [A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets](https://mlsanity.com/paper/2006.11880/) (2020)
- [Metrics for Graph Comparison: A Practitioner's Guide](https://mlsanity.com/paper/1904.07414/) (2019)
- [A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics](https://mlsanity.com/paper/2207.12599/) (2022)
- [Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer](https://mlsanity.com/paper/2110.10668/) (2021)
- [Evaluation Metrics for Unsupervised Learning Algorithms](https://mlsanity.com/paper/1905.05667/) (2019)
- [Insights into Performance Fitness and Error Metrics for Machine Learning](https://mlsanity.com/paper/2006.00887/) (2020)
- [Notes on the H-measure of classifier performance](https://mlsanity.com/paper/2106.11888/) (2021)
- [A Unified Study of Machine Learning Explanation Evaluation Metrics](https://mlsanity.com/paper/2203.14265/) (2022)
- [Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics](https://mlsanity.com/paper/2201.13066/) (2022)
- [Jaccard/Tanimoto similarity test and estimation methods](https://mlsanity.com/paper/1903.11372/) (2019)
- [How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation](https://mlsanity.com/paper/2209.06517/) (2022)
- [Survey of Imbalanced Data Methodologies](https://mlsanity.com/paper/2104.02240/) (2021)
- [Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data](https://mlsanity.com/paper/2208.11904/) (2022)

## Built on by (most cited first, 2 of 2)

- [Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting](https://mlsanity.com/paper/2606.09809/) (2026)
- [STABLEVAL: Disagreement-Aware and Stable Evaluation of AI Systems](https://mlsanity.com/paper/2605.02122/) (2026)

## Abstract (arXiv)

Machine Learning is a diverse field applied across various domains such as computer science, social sciences, medicine, chemistry, and finance. This diversity results in varied evaluation approaches, making it difficult to compare models effectively. Absolute evaluation measures offer a practical solution by assessing a model's performance on a fixed scale, independent of reference models and data ranges, enabling explicit comparisons. However, many commonly used measures are not universally applicable, leading to a lack of comprehensive guidance on their appropriate use. This survey addresses this gap by providing an overview of absolute evaluation metrics in ML, organized by the type of learning problem. While classification metrics have been extensively studied, this work also covers clustering, regression, and ranking metrics. By grouping these measures according to the specific ML challenges they address, this survey aims to equip practitioners with the tools necessary to select appropriate metrics for their models. The provided overview thus improves individual model evaluation and facilitates meaningful comparisons across different models and applications.