TrainImagesClassifier (dt)¶

Description¶

<put algortithm description here>

Parameters¶

Input Image List [multipleinput: rasters]

<put parameter description here>

Input Vector Data List [multipleinput: any vectors]

<put parameter description here>

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:

• 0 — dt

Default: 0

Maximum depth of the tree [number]

<put parameter description here>

Default: 65535

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: 0.01

Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split [number]

<put parameter description here>

Default: 10

K-fold cross-validations [number]

<put parameter description here>

Default: 10

Set Use1seRule flag to false [boolean]

<put parameter description here>

Default: True

Set TruncatePrunedTree flag to false [boolean]

<put parameter description here>

Default: True

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:trainimagesclassifierdt', -io.il, -io.vd, -io.imstat, -elev.default, -sample.mt, -sample.mv, -sample.edg, -sample.vtr, -sample.vfn, -classifier, -classifier.dt.max, -classifier.dt.min, -classifier.dt.ra, -classifier.dt.cat, -classifier.dt.f, -classifier.dt.r, -classifier.dt.t, -rand, -io.confmatout, -io.out)

See also¶