mlsanityData to 7 Oct 2026

stream-learn -- open-source Python library for difficult data stream batch analysis

arXiv 2001.11077 ↗First posted 29 Jan 2020cs.LG cs.CV stat.MLcited by 12

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Abstract (arXiv)

stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incremental drift) in their recurring or non-recurring versions. The package allows conducting experiments following established evaluation methodologies (i.e. Test-Then-Train and Prequential). In addition, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-art chunk-based and online classifier ensembles. To improve computational efficiency, package utilises its own implementations of prediction metrics for imbalanced binary classification tasks.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 4; 2025: 0; 2026: 2.

12 papers.