Fit MinMaxScaler

Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

Parameters

Transformer [string]

Scikit-learn python code. See MinMaxScaler for information on different parameters.

Default:

from sklearn.preprocessing import MinMaxScaler

transformer = MinMaxScaler(feature_range=(0, 1), clip=False)

Raster layer with features [raster]

Raster layer with feature data X used for fitting the transformer. Mutually exclusive with parameter: Training dataset

Sample size [number]

Approximate number of samples drawn from raster. If 0, whole raster will be used. Note that this is only a hint for limiting the number of rows and columns.

Default: 1000

Training dataset [file]

Training dataset pickle file used for fitting the transformer. Mutually exclusive with parameter: Raster layer with features

Outputs

Output transformer [fileDestination]

Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitMinmaxscaler:

----------------
Arguments
----------------

transformer: Transformer
    Default value:  from sklearn.preprocessing import MinMaxScaler

transformer = MinMaxScaler(feature_range=(0, 1), clip=False)
    Argument type:  string
    Acceptable values:
            - String value
featureRaster: Raster layer with features (optional)
    Argument type:  raster
    Acceptable values:
            - Path to a raster layer
sampleSize: Sample size (optional)
    Default value:  1000
    Argument type:  number
    Acceptable values:
            - A numeric value
dataset: Training dataset (optional)
    Argument type:  file
    Acceptable values:
            - Path to a file
outputTransformer: Output transformer
    Argument type:  fileDestination
    Acceptable values:
            - Path for new file

----------------
Outputs
----------------

outputTransformer: <outputFile>
    Output transformer