# Frouros: A Python library for drift detection in machine learning systems

arXiv: https://arxiv.org/abs/2208.06868 · HTML: https://mlsanity.com/paper/2208.06868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Learning Deep Kernels for Non-Parametric Two-Sample Tests](https://mlsanity.com/paper/2002.09116/) (2020)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [Automatically detecting data drift in machine learning classifiers](https://mlsanity.com/paper/2111.05672/) (2021)
- [Context-Aware Drift Detection](https://mlsanity.com/paper/2203.08644/) (2022)
- [Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection](https://mlsanity.com/paper/2106.12893/) (2021)

## Built on by (most cited first, 1 of 1)

- [Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift](https://mlsanity.com/paper/2412.09035/) (2024)

## Abstract (arXiv)

Frouros is an open-source Python library capable of detecting drift in machine learning systems. It provides a combination of classical and more recent algorithms for drift detection: both concept and data drift. We have designed it with the objective of making it compatible with any machine learning framework and easily adaptable to real-world use cases. The library is developed following a set of best development and continuous integration practices to ensure ease of maintenance and extensibility. The source code is available at https://github.com/IFCA/frouros.