diff --git a/README.md b/README.md
index 965436c..2107b88 100644
--- a/README.md
+++ b/README.md
@@ -49,13 +49,13 @@ conformal_predictor.fit(X_train, y_train)
 
 When the input data is a pandas DataFrame, the output is also a pandas DataFrame. For example, printing the head of `ŷ_test_quantiles` yields:
 
-|   house_id |   0.025 |   0.05 |    0.1 |    0.9 |   0.95 |   0.975 |
-|-----------:|--------:|-------:|-------:|-------:|-------:|--------:|
-|       1357 |  114784 | 120894 | 131618 | 175761 | 188052 |  205449 |
-|       2367 |   67417 |  80074 |  86754 | 117854 | 127583 |  142322 |
-|       2822 |  119423 | 132048 | 138725 | 178526 | 197246 |  214206 |
-|       2126 |   94031 |  99850 | 110891 | 150249 | 164703 |  182528 |
-|       1544 |   68996 |  81516 |  88232 | 121774 | 132425 |  147110 |
+|   house_id |    0.025 |     0.05 |      0.1 |    0.9 |   0.95 |   0.975 |
+|-----------:|---------:|---------:|---------:|-------:|-------:|--------:|
+|       1357 | 114784   | 120894   | 131618   | 175761 | 188052 |  205449 |
+|       2367 |  67416.6 |  80073.7 |  86754   | 117854 | 127583 |  142322 |
+|       2822 | 119423   | 132048   | 138725   | 178526 | 197246 |  214206 |
+|       2126 |  94030.6 |  99850   | 110891   | 150249 | 164703 |  182528 |
+|       1544 |  68996.2 |  81516.3 |  88231.6 | 121774 | 132425 |  147110 |
 
 Let's visualize the predicted quantiles on the test set:
 
@@ -114,13 +114,13 @@ print(coverage)  # 96.6%
 
 When the input data is a pandas DataFrame, the output is also a pandas DataFrame. For example, printing the head of `ŷ_test_interval` yields:
 
-|   house_id |   0.025 |   0.975 |
-|-----------:|--------:|--------:|
-|       1357 |  107203 |  206290 |
-|       2367 |   66665 |  146005 |
-|       2822 |  115592 |  220315 |
-|       2126 |   85288 |  183038 |
-|       1544 |   67890 |  150646 |
+|   house_id |    0.025 |   0.975 |
+|-----------:|---------:|--------:|
+|       1357 | 107203   |  206290 |
+|       2367 |  66665.1 |  146005 |
+|       2822 | 115592   |  220315 |
+|       2126 |  85288.1 |  183038 |
+|       1544 |  67889.9 |  150646 |
 
 ## Contributing
 
diff --git a/src/conformal_tights/_conformal_coherent_quantile_regressor.py b/src/conformal_tights/_conformal_coherent_quantile_regressor.py
index 4894d0a..0c945a7 100644
--- a/src/conformal_tights/_conformal_coherent_quantile_regressor.py
+++ b/src/conformal_tights/_conformal_coherent_quantile_regressor.py
@@ -101,11 +101,10 @@ def fit(
         y = np.ravel(np.asarray(y))
         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))
         y = y.astype(np.float64)  # To support datetime64[ns] and timedelta64[ns].
         if sample_weight is not None:
             check_consistent_length(y, sample_weight)
-            sample_weight = np.ravel(np.asarray(sample_weight).astype(np.float64))
+            sample_weight = np.ravel(np.asarray(sample_weight).astype(y.dtype))
         # Use the smallest of the relative and absolute calibration sizes.
         calib_size = min(
             int(self.conformal_calibration_size[0] * X.shape[0]), self.conformal_calibration_size[1]
@@ -274,9 +273,8 @@ 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_ and np.issubdtype(self.y_dtype_, np.integer):
-            ŷ_quantiles = np.round(ŷ_quantiles)
-        ŷ_quantiles = ŷ_quantiles.astype(self.y_dtype_)
+        if not np.issubdtype(self.y_dtype_, np.integer):
+            ŷ_quantiles = ŷ_quantiles.astype(self.y_dtype_)
         # Convert ŷ_quantiles to a pandas DataFrame if X is a pandas DataFrame.
         if hasattr(X, "dtypes") and hasattr(X, "index"):
             try:
@@ -354,9 +352,8 @@ def predict(
             ŷ_quantiles = self.predict_quantiles(X, quantiles=quantiles)
             return ŷ_quantiles
         ŷ = self.estimator_.predict(X)
-        if self.y_is_integer_:
-            ŷ = np.round(ŷ)
-        ŷ = ŷ.astype(self.y_dtype_)
+        if not np.issubdtype(self.y_dtype_, np.integer):
+            ŷ = ŷ.astype(self.y_dtype_)
         if hasattr(X, "dtypes") and hasattr(X, "index"):
             try:
                 import pandas as pd
@@ -369,4 +366,9 @@ def predict(
 
     def _more_tags(self) -> dict[str, Any]:
         """Return more tags for the estimator."""
-        return {"allow_nan": True, "_xfail_checks": {"check_regressors_int": "Incompatible check"}}
+        return {
+            "allow_nan": True,
+            "_xfail_checks": {
+                "check_sample_weights_invariance": "Conformal calibration not invariant to removing zero-weight examples"
+            },
+        }