Frouros: A Python library for drift detection in machine learning systems
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
13 of 67 references.
References · 13
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016Revisiting Classifier Two-Sample Testscited by 268
- 2018Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shiftcited by 243
- 2020Learning Deep Kernels for Non-Parametric Two-Sample Testscited by 130
- 2020River: machine learning for streaming data in Pythoncited by 98
- 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 83
- 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
- 2020Reactive Soft Prototype Computing for Concept Drift Streamscited by 40
- 2017On the Reliable Detection of Concept Drift from Streaming Unlabeled Datacited by 33
- 2015Concept Drift Detection for Streaming Datacited by 18
- 2021Automatically detecting data drift in machine learning classifierscited by 15
- 2022Context-Aware Drift Detectioncited by 9
Built on by
Citing papers per year: 2024: 1.
1 paper.