scikit-learn
diff --git a/‎sklearn/_loss/loss.py
Lines changed: 18 additions & 5 deletions b/‎sklearn/_loss/loss.py
Lines changed: 18 additions & 5 deletions
diff --git a/‎sklearn/_loss/tests/test_loss.py
Lines changed: 36 additions & 0 deletions b/‎sklearn/_loss/tests/test_loss.py
Lines changed: 36 additions & 0 deletions
@@ -15,6 +15,7 @@
 #   - SGDRegressor, SGDClassifier
 # - Replace link module of GLMs.
 
+import numbers
 import numpy as np
 from scipy.special import xlogy
 from ._loss import (
@@ -34,6 +35,7 @@
     LogitLink,
     MultinomialLogit,
 )
+from ..utils import check_scalar
 from ..utils._readonly_array_wrapper import ReadonlyArrayWrapper
 from ..utils.stats import _weighted_percentile
 
@@ -604,11 +606,14 @@ class PinballLoss(BaseLoss):
     need_update_leaves_values = True
 
     def __init__(self, sample_weight=None, quantile=0.5):
-        if quantile <= 0 or quantile >= 1:
-            raise ValueError(
-                "PinballLoss aka quantile loss only accepts "
-                f"0 < quantile < 1; {quantile} was given."
-            )
+        check_scalar(
+            quantile,
+            "quantile",
+            target_type=numbers.Real,
+            min_val=0,
+            max_val=1,
+            include_boundaries="neither",
+        )
         super().__init__(
             closs=CyPinballLoss(quantile=float(quantile)),
             link=IdentityLink(),
@@ -725,6 +730,14 @@ class HalfTweedieLoss(BaseLoss):
     """
 
     def __init__(self, sample_weight=None, power=1.5):
+        check_scalar(
+            power,
+            "power",
+            target_type=numbers.Real,
+            include_boundaries="neither",
+            min_val=-np.inf,
+            max_val=np.inf,
+        )
         super().__init__(
             closs=CyHalfTweedieLoss(power=float(power)),
             link=LogLink(),
 
@@ -1048,6 +1048,42 @@ def test_init_gradient_and_hessian_raises(loss, params, err_msg):
         gradient, hessian = loss.init_gradient_and_hessian(n_samples=5, **params)
 
 
+@pytest.mark.parametrize(
+    "loss, params, err_type, err_msg",
+    [
+        (
+            PinballLoss,
+            {"quantile": None},
+            TypeError,
+            "quantile must be an instance of float, not NoneType.",
+        ),
+        (
+            PinballLoss,
+            {"quantile": 0},
+            ValueError,
+            "quantile == 0, must be > 0.",
+        ),
+        (PinballLoss, {"quantile": 1.1}, ValueError, "quantile == 1.1, must be < 1."),
+        (
+            HalfTweedieLoss,
+            {"power": None},
+            TypeError,
+            "power must be an instance of float, not NoneType.",
+        ),
+        (
+            HalfTweedieLoss,
+            {"power": np.inf},
+            ValueError,
+            "power == inf, must be < inf.",
+        ),
+    ],
+)
+def test_loss_init_parameter_validation(loss, params, err_type, err_msg):
+    """Test that loss raises errors for invalid input."""
+    with pytest.raises(err_type, match=err_msg):
+        loss(**params)
+
+
 @pytest.mark.parametrize("loss", LOSS_INSTANCES, ids=loss_instance_name)
 def test_loss_pickle(loss):
     """Test that losses can be pickled."""