# Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement

arXiv: https://arxiv.org/abs/2206.02423 · HTML: https://mlsanity.com/paper/2206.02423/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 6 Jun 2022.

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

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Instance-Dependent PU Learning by Bayesian Optimal Relabeling](https://mlsanity.com/paper/1808.02180/) (2018)
- [A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models](https://mlsanity.com/paper/1402.3144/) (2014)

## Built on by (most cited first, 4 of 4)

- [Towards Improved Illicit Node Detection with Positive-Unlabelled Learning](https://mlsanity.com/paper/2303.02462/) (2023)
- [Positive-Unlabelled Learning for identifying new candidate Dietary Restriction-related genes among Ageing-related genes](https://mlsanity.com/paper/2406.09898/) (2024)
- [Automated Machine Learning for Positive-Unlabelled Learning](https://mlsanity.com/paper/2401.06452/) (2024)
- [Learning from sanctioned government suppliers: A machine learning and network science approach to detecting fraud and corruption in Mexico](https://mlsanity.com/paper/2512.19491/) (2025)

## Abstract (arXiv)

Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.