openModeller id: RF
Current version: 0.2 Developer(s): Renato De Giovanni
Accepts Categorical Maps: no
Requires absence points: no
Author(s): Leo Breiman & Adele Cutler
Random Forests
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
Number of trees
openModeller id: NumTrees
Number of trees
Data type: integer Domain: [1.0, 1000.0] Typical value: 10
Number of variables per tree
openModeller id: VarsPerTree
Number of variables per tree (zero defaults to the square root of the number of layers)
Data type: integer Domain: [oo, oo] Typical value: 0
Force unsupervised learning
openModeller id: ForceUnsupervisedLearning
When absence points are provided, this parameter can be used to ignore them forcing unsupervised learning. Note that if no absences are provided, unsupervised learning will be used anyway.
Data type: integer Domain: [0.0, 1.0] Typical value: 0
The following image shows a sample model in the environmental space (temperature x precipitation) generated with the standard dataset used for tests (Thalurania furcata boliviana localities dataset):
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| fig. 1: num. of trees = 10, num. of variables = 1 |