Fit RandomForestClassifier

Fits a Random Forest Classifier

See the following Cookbook Recipes on how to use classifiers: Classification , Graphical Modeler

Parameters

Raster [raster]

Raster with training data features.

Labels [raster]

Classification with training data labels.

Mask [layer]

Specified vector or raster is interpreted as a boolean mask.

In case of a vector, all pixels covered by features are interpreted as True, all other pixels as False.

In case of a raster, all pixels that are equal to the no data value (default is 0) are interpreted as False, all other pixels as True.Multiband rasters are first evaluated band wise. The final mask for a given pixel is True, if all band wise masks for that pixel are True.

Code [string]

Scikit-learn python code. See RandomForestClassifier for information on different parameters. If this code is not altered, scikit-learn default settings will be used. ‘Hint: you might want to alter e.g. the n_estimators value (number of trees), as the default is 10. So the line of code might be altered to ‘estimator = RandomForestClassifier(n_estimators=100).’

Default:

from sklearn.ensemble import RandomForestClassifier
estimator = RandomForestClassifier(n_estimators=100, oob_score=True)

Outputs

Output Classifier [fileDestination]

Specifiy output path for the classifier (.pkl). This file can be used for applying the classifier to an image using ‘Classification -> Predict Classification’ and ‘Classification -> Predict ClassFraction’.

Default: outEstimator.pkl