diff --git a/pyproject.toml b/pyproject.toml
index 05ec09b..87a78d5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -75,7 +75,7 @@ warn_unreachable = true
 
 [tool.pytest.ini_options]  # https://docs.pytest.org/en/latest/reference/reference.html#ini-options-ref
 addopts = "--color=yes --doctest-modules --exitfirst --failed-first --strict-config --strict-markers --verbosity=2 --junitxml=reports/pytest.xml"
-filterwarnings = ["error", "ignore::DeprecationWarning"]
+filterwarnings = ["error", "ignore::DeprecationWarning", "ignore::sklearn.exceptions.SkipTestWarning"]
 testpaths = ["src", "tests"]
 xfail_strict = true
 
diff --git a/src/conformal_tights/_conformal_coherent_quantile_regressor.py b/src/conformal_tights/_conformal_coherent_quantile_regressor.py
index 6234952..4894d0a 100644
--- a/src/conformal_tights/_conformal_coherent_quantile_regressor.py
+++ b/src/conformal_tights/_conformal_coherent_quantile_regressor.py
@@ -1,6 +1,6 @@
 """Conformal Coherent Quantile Regressor meta-estimator."""
 
-from typing import TYPE_CHECKING, Literal, TypeVar, overload
+from typing import TYPE_CHECKING, Any, Literal, TypeVar, overload
 
 import numpy as np
 import numpy.typing as npt
@@ -102,8 +102,6 @@ def fit(
         self.n_features_in_: int = X.shape[1]
         self.y_dtype_: npt.DTypeLike = y.dtype  # Used to cast predictions to the correct dtype.
         self.y_is_integer_: bool = bool(np.all(y.astype(np.intp) == y))
-        if self.y_is_integer_:
-            self.y_dtype_ = np.intp  # To satisfy sklearn's `check_regressors_int`.
         y = y.astype(np.float64)  # To support datetime64[ns] and timedelta64[ns].
         if sample_weight is not None:
             check_consistent_length(y, sample_weight)
@@ -276,7 +274,7 @@ def predict_quantiles(
             np.arange(Δŷ_quantiles.shape[0]), :, np.argmin(dispersion, axis=-1)
         ]
         ŷ_quantiles: FloatMatrix[F] = ŷ[:, np.newaxis] + Δŷ_quantiles
-        if self.y_is_integer_:
+        if self.y_is_integer_ and np.issubdtype(self.y_dtype_, np.integer):
             ŷ_quantiles = np.round(ŷ_quantiles)
         ŷ_quantiles = ŷ_quantiles.astype(self.y_dtype_)
         # Convert ŷ_quantiles to a pandas DataFrame if X is a pandas DataFrame.
@@ -369,6 +367,6 @@ def predict(
                 return ŷ_series
         return ŷ
 
-    def _more_tags(self) -> dict[str, bool]:
+    def _more_tags(self) -> dict[str, Any]:
         """Return more tags for the estimator."""
-        return {"allow_nan": True}
+        return {"allow_nan": True, "_xfail_checks": {"check_regressors_int": "Incompatible check"}}