Random Forests

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

Sample models

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):

fig. 1: num. of trees = 10, num. of variables = 1