diff --git a/sklearn2pmml/decoration/__init__.py b/sklearn2pmml/decoration/__init__.py
index fb9e251..00c83be 100644
--- a/sklearn2pmml/decoration/__init__.py
+++ b/sklearn2pmml/decoration/__init__.py
@@ -161,7 +161,8 @@ def _valid_value_mask(self, X, where):
 			if hasattr(X, "isin"):
 				mask = X.isin(self.data_)
 			else:
-				mask = numpy.isin(X, self.data_)
+				mask = numpy.full(X.shape, fill_value = False)
+				mask[where] = numpy.isin(X[where], self.data_)
 			return numpy.logical_and(mask, where)
 		return super(DiscreteDomain, self)._valid_value_mask(X, where)
 
diff --git a/sklearn2pmml/decoration/tests/__init__.py b/sklearn2pmml/decoration/tests/__init__.py
index 198eadd..a772346 100644
--- a/sklearn2pmml/decoration/tests/__init__.py
+++ b/sklearn2pmml/decoration/tests/__init__.py
@@ -11,6 +11,7 @@
 from unittest import TestCase
 
 import numpy
+import pandas
 
 class AliasTest(TestCase):
 
@@ -113,6 +114,20 @@ def test_fit_int_missing(self):
 		Xt = domain.transform(X)
 		self.assertEqual([0, 0, 2], Xt.tolist())
 
+	def test_fit_int64(self):
+		domain = clone(CategoricalDomain())
+		X = Series([-1, None, 1, 2, -1]).astype("Int64")
+		self.assertEqual([False, True, False, False, False], domain._missing_value_mask(X).tolist())
+		Xt = domain.fit_transform(X)
+		self.assertEqual([-1, 1, 2], domain.data_.tolist())
+		self.assertEqual([-1, pandas.NA, 1, 2, -1], Xt.tolist())
+		domain = clone(CategoricalDomain())
+		X = X.to_numpy()
+		self.assertEqual([False, True, False, False, False], domain._missing_value_mask(X).tolist())
+		Xt = domain.fit_transform(X)
+		self.assertEqual([-1, 1, 2], domain.data_.tolist())
+		self.assertEqual([-1, pandas.NA, 1, 2, -1], Xt.tolist())
+
 	def test_fit_int_categorical(self):
 		domain = clone(CategoricalDomain(dtype = CategoricalDtype()))
 		self.assertIsNone(domain.dtype.categories)