ml-rapids: Incremental learning written in C++ exposed in Python
ml-rapids implements incremental learning methods in C++ and exposes them via SWIG in Python. Installation can be achieved simply with
pip install ml_rapids. You can test your installation with running Python:
# testing ml-rapids import ml_rapids ml_rapids.test()
Further documentation is available here:
Implemented incremental learning methods
- Majority Class
- Naive Bayes
- Logistic Regression
- VFDT (Very Fast Decision Trees) aka Hoeffding Trees
- HAT (Hoeffding Adaptive Trees)
All the methods implement
sklearn incremantal learner interface (includes
Streaming random forest on top of Hoeffding trees will be implemented.
The library will be exposed via also via
Development notes can be read here.
Python deployment notes can be read here.
ml-rapids is developed by AILab at Jozef Stefan Institute.
This repository is based strongly on streamDM-cpp.