In the sklearn library, when using the Pipeline class, it is possible to modify the parameters of the nested estimators. This
modification can be done by using the Pipeline method set_params and specifying the name of the estimator and the parameter
to update separated by a double underscore __.
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
pipe = Pipeline(steps=[("clf", SVC())])
pipe.set_params(clf__C=10)
In the example above, the regularization parameter C is set to the value 10 for the classifier called clf.
Setting such parameters can be done as well with the help of the param_grid parameter for example when using
GridSearchCV.
Providing invalid parameters that do not exist on the estimator can lead to unexpected behavior or runtime errors.
This rule checks that the parameters provided to the set_params method of a Pipeline instance or through the param_grid
parameters of a GridSearchCV are valid for the nested estimators.
