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.
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
