Comprehensive collection of state-of-the-art algorithms for online learning, active learning, and concept drift adaptation. Designed for researchers and practitioners working with dynamic data streams.
Our toolkit stands out with these unique advantages for your online learning research and applications
Implements the latest algorithms from top-tier publications, keeping you at the forefront of online learning research.
Covers all aspects of online learning from active learning to drift adaptation in one unified toolkit.
Well-documented with intuitive APIs and examples to get you started quickly with minimal setup.
Designed to handle large-scale data streams efficiently with optimized memory usage and processing.
Modular architecture makes it easy to implement and test your own algorithms alongside ours.
Active development and growing community of researchers and practitioners for support and collaboration.
Compare results from multiple models in dynamic streaming environments with our interactive tools
Our visualization tools allow you to compare the performance of different models in real-time as they adapt to concept drift in data streams. The dynamic GIF demonstrates how various algorithms respond to changes in the underlying data distribution.
Key metrics such as accuracy, F1-score, MSE, and R² are tracked and visualized to help researchers and practitioners understand model behavior under different conditions.
