scikit-learn
diff --git a/‎sklearn/metrics/cluster/_supervised.py
Lines changed: 3 additions & 3 deletions b/‎sklearn/metrics/cluster/_supervised.py
Lines changed: 3 additions & 3 deletions
diff --git a/‎sklearn/utils/_array_api.py
Lines changed: 12 additions & 0 deletions b/‎sklearn/utils/_array_api.py
Lines changed: 12 additions & 0 deletions
diff --git a/‎sklearn/utils/tests/test_array_api.py
Lines changed: 10 additions & 0 deletions b/‎sklearn/utils/tests/test_array_api.py
Lines changed: 10 additions & 0 deletions
@@ -15,7 +15,7 @@
 import numpy as np
 from scipy import sparse as sp
 
-from ...utils._array_api import get_namespace
+from ...utils._array_api import _max_precision_float_dtype, get_namespace_and_device
 from ...utils._param_validation import Interval, StrOptions, validate_params
 from ...utils.multiclass import type_of_target
 from ...utils.validation import check_array, check_consistent_length
@@ -1275,12 +1275,12 @@ def entropy(labels):
     -----
     The logarithm used is the natural logarithm (base-e).
     """
-    xp, is_array_api_compliant = get_namespace(labels)
+    xp, is_array_api_compliant, device_ = get_namespace_and_device(labels)
     labels_len = labels.shape[0] if is_array_api_compliant else len(labels)
     if labels_len == 0:
         return 1.0
 
-    pi = xp.astype(xp.unique_counts(labels)[1], xp.float64)
+    pi = xp.astype(xp.unique_counts(labels)[1], _max_precision_float_dtype(xp, device_))
 
     # single cluster => zero entropy
     if pi.size == 1:
 
@@ -609,6 +609,18 @@ def _add_to_diagonal(array, value, xp):
             array[i, i] += value
 
 
+def _max_precision_float_dtype(xp, device):
+    """Return the float dtype with the highest precision supported by the device."""
+    # TODO: Update to use `__array_namespace__info__()` from array-api v2023.12
+    # when/if that becomes more widespread.
+    xp_name = xp.__name__
+    if xp_name in {"array_api_compat.torch", "torch"} and (
+        str(device).startswith("mps")
+    ):  # pragma: no cover
+        return xp.float32
+    return xp.float64
+
+
 def _find_matching_floating_dtype(*arrays, xp):
     """Find a suitable floating point dtype when computing with arrays.
 
 
@@ -17,6 +17,7 @@
     _estimator_with_converted_arrays,
     _is_numpy_namespace,
     _isin,
+    _max_precision_float_dtype,
     _nanmax,
     _nanmin,
     _NumPyAPIWrapper,
@@ -510,6 +511,15 @@ def test_indexing_dtype(namespace, _device, _dtype):
         assert indexing_dtype(xp) == xp.int64
 
 
+@pytest.mark.parametrize(
+    "namespace, _device, _dtype", yield_namespace_device_dtype_combinations()
+)
+def test_max_precision_float_dtype(namespace, _device, _dtype):
+    xp = _array_api_for_tests(namespace, _device)
+    expected_dtype = xp.float32 if _device == "mps" else xp.float64
+    assert _max_precision_float_dtype(xp, _device) == expected_dtype
+
+
 @pytest.mark.parametrize(
     "array_namespace, device, _", yield_namespace_device_dtype_combinations()
 )