scikit-learn
diff --git a/‎sklearn/ensemble/_gb.py
Lines changed: 14 additions & 6 deletions b/‎sklearn/ensemble/_gb.py
Lines changed: 14 additions & 6 deletions
diff --git a/‎sklearn/ensemble/tests/test_gradient_boosting.py
Lines changed: 5 additions & 3 deletions b/‎sklearn/ensemble/tests/test_gradient_boosting.py
Lines changed: 5 additions & 3 deletions
@@ -20,6 +20,7 @@
 #          Arnaud Joly, Jacob Schreiber
 # License: BSD 3 clause
 
+import math
 import warnings
 from abc import ABCMeta, abstractmethod
 from numbers import Integral, Real
@@ -64,11 +65,16 @@
 
 def _safe_divide(numerator, denominator):
     """Prevents overflow and division by zero."""
-    with np.errstate(divide="raise"):
-        try:
-            return numerator / denominator
-        except FloatingPointError:
-            return 0.0
+    try:
+        # Cast to Python float to trigger a ZeroDivisionError without relying
+        # on `np.errstate` that is not supported by Pyodide.
+        result = float(numerator) / float(denominator)
+        if math.isinf(result):
+            warnings.warn("overflow encountered in _safe_divide", RuntimeWarning)
+        return result
+    except ZeroDivisionError:
+        warnings.warn("divide by zero encountered in _safe_divide", RuntimeWarning)
+        return 0.0
 
 
 def _init_raw_predictions(X, estimator, loss, use_predict_proba):
@@ -235,7 +241,9 @@ def compute_update(y_, indices, neg_gradient, raw_prediction, k):
 
         # update each leaf (= perform line search)
         for leaf in np.nonzero(tree.children_left == TREE_LEAF)[0]:
-            indices = np.nonzero(terminal_regions == leaf)[0]  # of terminal regions
+            indices = np.nonzero(masked_terminal_regions == leaf)[
+                0
+            ]  # of terminal regions
             y_ = y.take(indices, axis=0)
             sw = None if sample_weight is None else sample_weight[indices]
             update = compute_update(y_, indices, neg_gradient, raw_prediction, k)
 
@@ -1445,11 +1445,13 @@ def test_huber_vs_mean_and_median():
 
 def test_safe_divide():
     """Test that _safe_divide handles division by zero."""
-    assert _safe_divide(np.array([1e300]), 0) == 0
-
+    with pytest.warns(RuntimeWarning, match="divide"):
+        assert _safe_divide(np.float64(1e300), 0) == 0
+    with pytest.warns(RuntimeWarning, match="divide"):
+        assert _safe_divide(np.float64(0.0), np.float64(0.0)) == 0
     with pytest.warns(RuntimeWarning, match="overflow"):
         # np.finfo(float).max = 1.7976931348623157e+308
-        _safe_divide(np.array([1e300]), 1e-10)
+        _safe_divide(np.float64(1e300), 1e-10)
 
 
 def test_squared_error_exact_backward_compat():