TrainImagesClassifier (rf)¶

Description¶

<put algortithm description here>

Parameters¶

Input Image List [multipleinput: rasters]

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Input Vector Data List [multipleinput: any vectors]

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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]

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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:

• 0 — rf

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

Outputs¶

Output confusion matrix [file]

<put output description here>

Output model [file]

<put output description here>

Console usage¶

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)

See also¶