diff --git a/bob/pipelines/wrappers.py b/bob/pipelines/wrappers.py
index 885acfa95be99edabc1db5e81c8ba6f3e54a7188..0297c78c97057ef3f0e39d2e34a100920b08919a 100644
--- a/bob/pipelines/wrappers.py
+++ b/bob/pipelines/wrappers.py
@@ -153,6 +153,12 @@ class SampleWrapper(BaseWrapper, TransformerMixin):
         return self._samples_transform(samples, "score")
 
     def fit(self, samples, y=None):
+        if y is not None:
+            raise TypeError(
+                "We don't accept `y` in fit arguments because "
+                "`y` should be part of the sample. To pass `y` "
+                "to the wrapped estimator, use `fit_extra_arguments`."
+            )
 
         if is_estimator_stateless(self.estimator):
             return self
@@ -163,7 +169,7 @@ class SampleWrapper(BaseWrapper, TransformerMixin):
 
         X = SampleBatch(samples)
 
-        self.estimator = self.estimator.fit(X, y=y, **kwargs)
+        self.estimator = self.estimator.fit(X, **kwargs)
         copy_learned_attributes(self.estimator, self)
         return self
 
diff --git a/doc/checkpoint.rst b/doc/checkpoint.rst
index 0646c7b3068e9cac772b8a50a6d3a9d8c59b5466..2f3ed31527dea1cf1426bcfdffbf94afbe26e0f8 100644
--- a/doc/checkpoint.rst
+++ b/doc/checkpoint.rst
@@ -40,7 +40,7 @@ transformer.
    ...         print(f"Transforming {len(X)} samples ...")
    ...         return np.array(X) + np.array(sample_specific_offsets)
    ...
-   ...     def fit(self, X, y=None):
+   ...     def fit(self, X):
    ...         print("Fit was called!")
    ...         return self
 
diff --git a/doc/xarray.rst b/doc/xarray.rst
index c6fd4bd0fbc5f9c0341fa2d9943d2a6cd53caf36..53cd9613f1fb3e267ec0f33cdd8077d8cd76a3b4 100644
--- a/doc/xarray.rst
+++ b/doc/xarray.rst
@@ -445,7 +445,7 @@ provide dask-ml estimators, set ``input_dask_array`` as ``True``.
    >>> ds = pipeline.fit(dataset).predict(dataset)
    >>> ds = ds.compute()
    >>> correct_classification = np.array(ds.data == ds.target).sum()
-   >>> correct_classification > 90
+   >>> correct_classification > 85
    True
    >>> ds.dims == {"sample": 150}
    True