Fit RobustScaler

Fits a Robust Scaler (scales features using statistics that are robust to outliers). Click here for example. See also examples for different scaling methods.

See the following Cookbook Recipes on how to use transformers: Transformation

Parameters

Raster [raster]

Specify input raster.

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 RobustScaler for information on different parameters.

Default:

from sklearn.preprocessing import RobustScaler

estimator = RobustScaler()

Outputs

Output Transformer [fileDestination]

Specifiy output path for the transformer (.pkl). This file can be used for applying the transformer to an image using ‘Transformation -> Transform Raster’ and ‘Transformation -> InverseTransform Raster’.

Default: outEstimator.pkl