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Issue #19614: Warn on irrelevant parameters for linear kernel in SVC #28641

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Issue #19614: Warn on irrelevant parameters for linear kernel in SVC #28641

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17 changes: 17 additions & 0 deletions sklearn/svm/_classes.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import warnings
from warnings import warn
from numbers import Integral, Real

import numpy as np
Expand Down Expand Up @@ -307,6 +308,7 @@ def fit(self, X, y, sample_weight=None):
self : object
An instance of the estimator.
"""

X, y = self._validate_data(
X,
y,
Expand All @@ -315,8 +317,23 @@ def fit(self, X, y, sample_weight=None):
order="C",
accept_large_sparse=False,
)

check_classification_targets(y)

self.classes_ = np.unique(y)
if self.kernel == 'linear':
if self.gamma not in ('scale', 'auto'):
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Let's warn for all non-default values

Suggested change
if self.gamma not in ('scale', 'auto'):
if self.gamma != "scale":

warn(
"The 'gamma' parameter has been set but is not relevant for the 'linear' kernel. It will be ignored.",
UserWarning)
if self.coef0 != 0:
warn(
"The 'coef0' parameter has been set but is not relevant for the 'linear' kernel. It will be ignored.",
UserWarning)
if self.degree != 3:
warn(
"The 'degree' parameter has been set but is not relevant for the 'linear' kernel. It will be ignored.",
UserWarning)

_dual = _validate_dual_parameter(
self.dual, self.loss, self.penalty, self.multi_class, X
Expand Down