scikit-learn
diff --git a/‎sklearn/preprocessing/_polynomial.py
Lines changed: 14 additions & 11 deletions b/‎sklearn/preprocessing/_polynomial.py
Lines changed: 14 additions & 11 deletions
diff --git a/‎sklearn/preprocessing/tests/test_polynomial.py
Lines changed: 2 additions & 5 deletions b/‎sklearn/preprocessing/tests/test_polynomial.py
Lines changed: 2 additions & 5 deletions
diff --git a/‎sklearn/tests/test_common.py
Lines changed: 0 additions & 1 deletion b/‎sklearn/tests/test_common.py
Lines changed: 0 additions & 1 deletion
@@ -2,7 +2,6 @@
 This file contains preprocessing tools based on polynomials.
 """
 import collections
-import numbers
 from numbers import Integral
 from itertools import chain, combinations
 from itertools import combinations_with_replacement as combinations_w_r
@@ -16,8 +15,8 @@
 from ..utils import check_array
 from ..utils.deprecation import deprecated
 from ..utils.validation import check_is_fitted, FLOAT_DTYPES, _check_sample_weight
-from ..utils._param_validation import Interval, StrOptions
 from ..utils.validation import _check_feature_names_in
+from ..utils._param_validation import Interval, StrOptions
 from ..utils.stats import _weighted_percentile
 
 from ._csr_polynomial_expansion import _csr_polynomial_expansion
@@ -131,6 +130,13 @@ class PolynomialFeatures(TransformerMixin, BaseEstimator):
            [ 1.,  4.,  5., 20.]])
     """
 
+    _parameter_constraints = {
+        "degree": [Interval(Integral, 0, None, closed="left"), "array-like"],
+        "interaction_only": ["boolean"],
+        "include_bias": ["boolean"],
+        "order": [StrOptions({"C", "F"})],
+    }
+
     def __init__(
         self, degree=2, *, interaction_only=False, include_bias=True, order="C"
     ):
@@ -286,14 +292,11 @@ def fit(self, X, y=None):
         self : object
             Fitted transformer.
         """
+        self._validate_params()
         _, n_features = self._validate_data(X, accept_sparse=True).shape
 
-        if isinstance(self.degree, numbers.Integral):
-            if self.degree < 0:
-                raise ValueError(
-                    f"degree must be a non-negative integer, got {self.degree}."
-                )
-            elif self.degree == 0 and not self.include_bias:
+        if isinstance(self.degree, Integral):
+            if self.degree == 0 and not self.include_bias:
                 raise ValueError(
                     "Setting degree to zero and include_bias to False would result in"
                     " an empty output array."
@@ -306,8 +309,8 @@ def fit(self, X, y=None):
         ):
             self._min_degree, self._max_degree = self.degree
             if not (
-                isinstance(self._min_degree, numbers.Integral)
-                and isinstance(self._max_degree, numbers.Integral)
+                isinstance(self._min_degree, Integral)
+                and isinstance(self._max_degree, Integral)
                 and self._min_degree >= 0
                 and self._min_degree <= self._max_degree
             ):
@@ -319,7 +322,7 @@ def fit(self, X, y=None):
                 )
             elif self._max_degree == 0 and not self.include_bias:
                 raise ValueError(
-                    "Setting both min_deree and max_degree to zero and include_bias to"
+                    "Setting both min_degree and max_degree to zero and include_bias to"
                     " False would result in an empty output array."
                 )
         else:
 
@@ -366,13 +366,10 @@ def test_spline_transformer_n_features_out(n_knots, include_bias, degree):
 @pytest.mark.parametrize(
     "params, err_msg",
     [
-        ({"degree": -1}, "degree must be a non-negative integer"),
-        ({"degree": 2.5}, "degree must be a non-negative int or tuple"),
-        ({"degree": "12"}, r"degree=\(min_degree, max_degree\) must"),
-        ({"degree": "string"}, "degree must be a non-negative int or tuple"),
         ({"degree": (-1, 2)}, r"degree=\(min_degree, max_degree\) must"),
         ({"degree": (0, 1.5)}, r"degree=\(min_degree, max_degree\) must"),
         ({"degree": (3, 2)}, r"degree=\(min_degree, max_degree\) must"),
+        ({"degree": (1, 2, 3)}, r"int or tuple \(min_degree, max_degree\)"),
     ],
 )
 def test_polynomial_features_input_validation(params, err_msg):
@@ -816,7 +813,7 @@ def test_polynomial_features_behaviour_on_zero_degree():
 
     poly = PolynomialFeatures(degree=(0, 0), include_bias=False)
     err_msg = (
-        "Setting both min_deree and max_degree to zero and include_bias to"
+        "Setting both min_degree and max_degree to zero and include_bias to"
         " False would result in an empty output array."
     )
     with pytest.raises(ValueError, match=err_msg):
 
@@ -495,7 +495,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
     "OneVsRestClassifier",
     "PatchExtractor",
     "PolynomialCountSketch",
-    "PolynomialFeatures",
     "RANSACRegressor",
     "RBFSampler",
     "RFE",