River - a library for data stream mining¶
Dr Jacob Montiel
- Incremental learning - All the tools in river can be updated with a single sample at a time.
- Adaptive learning - Adaptive methods are specifically designed to be robust against concept drift in dynamic environments.
- General-purpose - River caters for different machine learning problems, including regression, classification, unsupervised learning, and ad-hoc tasks.
- Efficient - By design, streaming techniques efficiently handle resources such as memory and processing time, given the unbounded nature of data streams.
- Easy to use - River is intended for users with any experience level. As a machine learning package, it caters for practitioners as well as researchers.
- Expandable - River is a constantly evolving resource with new and updated tools providing additional, or improved, capabilities.
- From batch to stream learning.
- Evaluating model accuracy.
- Process training sample points one at a time.
- Python programming.
- Stream processing
- Basic concepts.
- Data pre-processing.
- Sample problem - NOAA weather data ('NEWWeather' dataset)
- Decision Trees.
- Pipelines (chaining sequences of operations).
- Visualising operations.
- Concept drift.