Mondrian Forest

An online random forest implementaion written in Python.

Usage

import mondrianforest
from sklearn import datasets, cross_validation

iris = datasets.load_iris()
forest = mondrianforest.MondrianForestClassifier(n_tree=10)
cv = cross_validation.ShuffleSplit(len(iris.data), n_iter=20, test_size=0.10)
scores = cross_validation.cross_val_score(forest, iris.data, iris.target, cv=cv)
print(scores.mean(), scores.std())

License

mondrianforest is licensed under the MIT license.<br/> Copyright (c) 2016 nel215

References

Papers

• Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh, Mondrian Forests: Efficient Online Random Forests, Advances in Neural Information Processing Systems 27 (NIPS), pages 3140-3148, 2014
• Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh, Mondrian Forests for Large-Scale Regression when Uncertainty Matters, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 51
• Matej Balog, Yee Whye Teh, The Mondrian Process for Machine Learning

Slides

• Mondrian Forests

Videos

• Mondrian forests: Efficient random forests for streaming data via Bayesian nonparametrics

Code

• balajiln/mondrianforest