<put algortithm description here>
Input Image List [multipleinput: rasters]Input Vector Data List [multipleinput: any vectors]Input XML image statistics file [file]Optional.
<put parameter description here>
Default elevation [number]<put parameter description here>
Default: 0
Maximum training sample size per class [number]<put parameter description here>
Default: 1000
Maximum validation sample size per class [number]<put parameter description here>
Default: 1000
On edge pixel inclusion [boolean]<put parameter description here>
Default: True
Training and validation sample ratio [number]<put parameter description here>
Default: 0.5
Name of the discrimination field [string]<put parameter description here>
Default: Class
Classifier to use for the training [selection]<put parameter description here>
Options:
Default: 0
Maximum depth of the tree [number]<put parameter description here>
Default: 5
Minimum number of samples in each node [number]<put parameter description here>
Default: 10
Termination Criteria for regression tree [number]<put parameter description here>
Default: 0
Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split [number]<put parameter description here>
Default: 10
Size of the randomly selected subset of features at each tree node [number]<put parameter description here>
Default: 0
Maximum number of trees in the forest [number]<put parameter description here>
Default: 100
Sufficient accuracy (OOB error) [number]<put parameter description here>
Default: 0.01
set user defined seed [number]<put parameter description here>
Default: 0
Output confusion matrix [file]Output model [file]processing.runalg('otb:trainimagesclassifierrf', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.rf.max, -classifier.rf.min, -classifier.rf.ra, -classifier.rf.cat, -classifier.rf.var, -classifier.rf.nbtrees, -classifier.rf.acc, -rand, -io.confmatout, -io.out)