TAIAO

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.
Topics
- 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.
Additional Resources
- River Webpage: https://riverml.xyz/
- GitHub repository: https://github.com/online-ml/river/