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.
stream-learn -- open-source Python library for difficult data stream batch analysis
The stream-learn package enables the analysis of drifting and imbalanced data streams through synthetic data generation, established evaluation methodologies, and efficient implementation of specialized classifiers.
- Year
- 2020
- Venue
- arXiv 2020
- Authors
- 2
- Hosting
- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2001.11077ARXIV-DEFAULT
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- Semantic Scholar