# problexity -- an open-source Python library for binary classification problem complexity assessment

arXiv: https://arxiv.org/abs/2207.06709 · HTML: https://mlsanity.com/paper/2207.06709/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 14 Jul 2022.

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

- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [How Complex is your classification problem? A survey on measuring classification complexity](https://mlsanity.com/paper/1808.03591/) (2018)
- [Spectral Metric for Dataset Complexity Assessment](https://mlsanity.com/paper/1905.07299/) (2019)
- [An empirical study of the relation between network architecture and complexity](https://mlsanity.com/paper/1911.04120/) (2019)
- [Learning Abstract Task Representations](https://mlsanity.com/paper/2101.07852/) (2021)

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

- [A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms](https://mlsanity.com/paper/2307.01231/) (2023)
- [Clustering Indices based Automatic Classification Model Selection](https://mlsanity.com/paper/2305.13926/) (2023)

## Abstract (arXiv)

The classification problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning -- becoming the basis for determining meta-attributes or multi-criteria optimization -- allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 complexity measures for the Python language -- compatible with the scikit-learn programming interface -- allowing for the implementation of research using them in the most popular programming environment of the machine learning community.