Fit KernelRidge

Fits a KernelRidge Regression. Click here for additional information.

See the following Cookbook Recipes on how to use regressors: Regression

Parameters

Raster [raster]

Specify input raster.

Regression [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 KernelRidge for information on different parameters. See GridSearchCV for information on grid search and StandardScaler for scaling.

Default:

from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.kernel_ridge import KernelRidge

krr = KernelRidge()
param_grid = {'kernel': ['rbf'],
              'gamma': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
              'alpha': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
tunedKRR = GridSearchCV(cv=3, estimator=krr, scoring='neg_mean_absolute_error', param_grid=param_grid)
scaledAndTunedKRR = make_pipeline(StandardScaler(), tunedKRR)
estimator = scaledAndTunedKRR

Outputs

Output Regressor [fileDestination]

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

Default: outEstimator.pkl