/trees

A quick educational implementation of a random forest classifier and a decsion jungle classifier.

Primary LanguagePythonApache License 2.0Apache-2.0

trees

A quick educational implementation of a random forest classifier and a decision jungle classifier.

References:

  • A. Criminisi, J. Shotton, and E. Konukoglu, Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning. Foundations and Trends in Computer Graphics and Computer Vision. NOW Publishers. Vol.7: No 2-3, pp 81-227. 2012.

  • Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models for Classification, in Proc. NIPS, 2013.