An implementation of: Extremely randomized trees (Geurts et al. 2006) http://www.montefiore.ulg.ac.be/%7Eernst/uploads/news/id63/extremely-randomized-trees.pdf
Decision trees are a simple but powerful machine learning technique used in supervised classification. While the trees themselves are easy to comprehend after training, some implementations are prone to overfitting, and the training process can be slow. Extremely randomised trees are a very simple and easy implementation, but provide state of the art classification performance, are efficient to train, and are strongly resistent to overfitting.
Swift Forest deals with training examples of real valued features, and discrete output classes (i.e supervised classification). Individual trees or a forest (ensemble) can be constructed, and there are only 3 parameters:
- number of trees (if creating a forest, defaults to 100)
- minimum number of examples a node must have if it's to split (defaults to 2)
- number of features to randomly select during a split (defaults to sqrt(|features|))
Swift Forest uses the Swift package manager build system. Download a version of Swift that includes the build system (2.2+) and run:
$ swift buildThe command line interface is then available for testing:
$ ./.build/debug/cli help
Public Domain: To the extent possible under law, Tzukuri Pty Ltd has waived all copyright and related or neighbouring rights to SwiftForest. This work is published from: Australia.