scikit-learn · jeremiedbb · Mar 22, 2023 · Mar 21, 2023 · Mar 21, 2023 · Mar 21, 2023
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
@@ -1168,6 +1168,9 @@ def fit(self, X, y, sample_weight=None):
                "(penalty={})".format(self.penalty)
            )

+        if self.penalty == "elasticnet" and self.l1_ratio is None:
+            raise ValueError("l1_ratio must be specified when penalty is elasticnet.")
+
        # TODO(1.4): Remove "none" option
        if self.penalty == "none":
            warnings.warn(

diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
@@ -227,6 +227,15 @@ def test_check_solver_option(LR):
            lr.fit(X, y)


+@pytest.mark.parametrize("LR", [LogisticRegression, LogisticRegressionCV])
+def test_elasticnet_l1_ratio_err_helpful(LR):
+    # Check that an informative error message is raised when penalty="elasticnet"
+    # but l1_ratio is not specified.
+    model = LR(penalty="elasticnet", solver="saga")
+    with pytest.raises(ValueError, match=r".*l1_ratio.*"):
+        model.fit(np.array([[1, 2], [3, 4]]), np.array([0, 1]))
+
+
 @pytest.mark.parametrize("solver", ["lbfgs", "newton-cg", "sag", "saga"])
 def test_multinomial_binary(solver):
    # Test multinomial LR on a binary problem.