{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"River provides online machine learning algorithms that learn from streaming data one sample at a time, including linear models, decision trees, anomaly detection, clustering, and time series forecasting.","skillfed_tags":["streaming-ml","concept-drift","online-learning"],"use_cases":["Real-time fraud detection systems that adapt to evolving fraud patterns without retraining on historical data","Sensor data anomaly detection in IoT deployments where data arrives continuously and must be processed one observation at a time","Adaptive recommendation systems that learn user preferences from streaming interaction events","Time series forecasting for stock prices or weather data where concept drift is common","Online clustering of streaming data for customer segmentation that evolves over time","Model validation and evaluation in production contexts where data distribution may shift"],"what_it_does":"River is a Python library for online machine learning on streaming data, designed for scenarios where models must learn continuously from new observations without revisiting historical data. It implements a wide range of algorithms\u2014linear models, decision trees, random forests, nearest neighbors, anomaly detection, clustering, and time series forecasting\u2014all optimized for the online learning paradigm where predictions and model updates interleave on single samples.\n\nThe library emphasizes user-friendliness and clarity over raw performance, with a core interface (`learn_one`/`predict_one`) that has no pandas dependency. It depends on scipy, numpy, and altair for visualization. River targets scenarios where concept drift matters, where production systems are event-driven, or where you need a model that adapts to new data without batch retraining. It requires Python 3.11 or above.","worth_installing":"Yes. River is worth installing if you need online machine learning on streaming data\u2014a genuinely different problem from batch learning. Active maintenance, no known vulnerabilities, permissive licensing, and prebuilt wheels across platforms make it low-friction. Start with it if concept drift, event-driven learning, or single-sample-at-a-time processing matches your use case; skip it if standard batch learning suffices."},"id":"river","links":{"html":"https://skillfed.io/packages/river","md":"https://skillfed.io/packages/river.md","pypi":"https://pypi.org/project/river/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-31","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"river","python_support":"supports_current","summary":"Online machine learning in Python"},"popularity":{"monthly_downloads":213412,"position":9437,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.25.0"}