Give feedback when `svm.SVC` is configured with kernel hyperparameters for a different kernel

During our class we noticed some students incorrectly configure some hyperparameters which are irrelevant to the kernel used, for example setting gamma when using a linear kernel. We think it could make sense for scikit-learn to give feedback to the user when non-effective settings are configured.

Describe the workflow you want to enable

from sklearn.datasets import load_iris
from sklearn.svm import SVC

x, y = load_iris(return_X_y=True)
clf = SVC(kernel='linear', gamma=1e-6)
clf.fit(x, y)
print(clf.score(x, y))

current output:

0.9933333333333333

proposed output, something similar to:

UserWarning: Gamma is set but not used because a linear kernel is configured.
0.9933333333333333

[PGijsbers]

I’ll update the PR when there is a new consensus (the PR has a FutureWarning instead of DeprecationWarning right now) 😃 just let me know

[NicolasHug]

the validation should happen in fit (more details here if you’re interested https://scikit-learn.org/stable/developers/develop.html#instantiation)

Also please make sure to write a non-regression test for the cases that are worth testing. The test should make sure that the error is now properly raised. Your snippet above is a great candidate.

Thanks!