# Extending F1 metric, probabilistic approach

arXiv: https://arxiv.org/abs/2210.11997 · HTML: https://mlsanity.com/paper/2210.11997/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [The MCC-F1 curve: a performance evaluation technique for binary classification](https://mlsanity.com/paper/2006.11278/) (2020)

## Built on by (most cited first, 12 of 12)

- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [The Tile: A 2D Map of Ranking Scores for Two-Class Classification](https://mlsanity.com/paper/2412.04309/) (2024)
- [A Call to Action for a Secure-by-Design Generative AI Paradigm](https://mlsanity.com/paper/2510.00451/) (2025)
- [Selecting a classification performance measure: matching the measure to the problem](https://mlsanity.com/paper/2409.12391/) (2024)
- [Classification under strategic adversary manipulation using pessimistic bilevel optimisation](https://mlsanity.com/paper/2410.20284/) (2024)
- [Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in text](https://mlsanity.com/paper/2311.06440/) (2023)
- [Is F1 Score Suboptimal for Cybersecurity Models? Introducing Cscore, a Cost-Aware Alternative for Model Assessment](https://mlsanity.com/paper/2407.14664/) (2024)
- [Next-Generation Teleophthalmology: AI-enabled Quality Assessment Aiding Remote Smartphone-based Consultation](https://mlsanity.com/paper/2402.07118/) (2024)
- [A weighted-likelihood framework for class imbalance in Bayesian prediction models](https://mlsanity.com/paper/2504.17013/) (2025)
- [Countering adversarial evasion in regression analysis](https://mlsanity.com/paper/2509.22113/) (2025)
- [Adversarial training with restricted data manipulation](https://mlsanity.com/paper/2510.03254/) (2025)
- [Cost-Sensitive Evaluation for Binary Classifiers](https://mlsanity.com/paper/2510.22016/) (2025)

## Abstract (arXiv)

This article explores the extension of well-known F1 score used for assessing the performance of binary classifiers. We propose the new metric using probabilistic interpretation of precision, recall, specificity, and negative predictive value. We describe its properties and compare it to common metrics. Then we demonstrate its behavior in edge cases of the confusion matrix. Finally, the properties of the metric are tested on binary classifier trained on the real dataset.