diff --git a/onnxmltools/convert/xgboost/operator_converters/XGBoost.py b/onnxmltools/convert/xgboost/operator_converters/XGBoost.py
index 1f343c7b..3c82d174 100644
--- a/onnxmltools/convert/xgboost/operator_converters/XGBoost.py
+++ b/onnxmltools/convert/xgboost/operator_converters/XGBoost.py
@@ -230,7 +230,7 @@ def convert(scope, operator, container):
 
         bst = xgb_node.get_booster()
         best_ntree_limit = getattr(bst, 'best_ntree_limit', len(js_trees)) * ncl
-        if best_ntree_limit < len(js_trees):
+        if 0 < best_ntree_limit < len(js_trees):
             js_trees = js_trees[:best_ntree_limit]
             attr_pairs = XGBClassifierConverter._get_default_tree_attribute_pairs()
             XGBConverter.fill_tree_attributes(js_trees, attr_pairs, [1 for _ in js_trees], True)
diff --git a/tests/xgboost/data_fail_empty.csv b/tests/xgboost/data_fail_empty.csv
new file mode 100644
index 00000000..b577f93c
--- /dev/null
+++ b/tests/xgboost/data_fail_empty.csv
@@ -0,0 +1,21 @@
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diff --git a/tests/xgboost/test_xgboost_13.py b/tests/xgboost/test_xgboost_13.py
new file mode 100644
index 00000000..3164a3ae
--- /dev/null
+++ b/tests/xgboost/test_xgboost_13.py
@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Tests scilit-learn's tree-based methods' converters.
+"""
+import os
+import unittest
+import numpy as np
+from numpy.testing import assert_almost_equal
+import pandas
+from sklearn.model_selection import train_test_split
+from xgboost import XGBRegressor, XGBClassifier, train, DMatrix
+from onnxmltools.convert import convert_xgboost
+from onnxmltools.convert.common.data_types import FloatTensorType
+from onnxruntime import InferenceSession
+
+
+class TestXGBoost13(unittest.TestCase):
+
+    def test_xgb_regressor(self):
+        this = os.path.dirname(__file__)
+        df = pandas.read_csv(os.path.join(this, "data_fail_empty.csv"))
+        X, y = df.drop('y', axis=1), df['y']
+        X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+        clr = XGBClassifier(
+            max_delta_step= 0, tree_method='hist', n_estimators=100,
+            booster='gbtree', objective='binary:logistic', eval_metric='logloss',
+            learning_rate= 0.1, gamma=10, max_depth=7, min_child_weight=50,
+            subsample=0.75, colsample_bytree=0.75, random_state=42,
+            verbosity=0)
+
+        clr.fit(X_train, y_train, eval_set=[(X_test, y_test)],
+                early_stopping_rounds=40)
+
+        initial_type = [('float_input', FloatTensorType([None, 797]))]
+        onx = convert_xgboost(clr, initial_types=initial_type)
+        expected = clr.predict(X_test), clr.predict_proba(X_test)
+        sess = InferenceSession(onx.SerializeToString())
+        X_test = X_test.values.astype(np.float32)
+        got = sess.run(None, {'float_input': X_test})
+        assert_almost_equal(expected[1], got[1])
+        assert_almost_equal(expected[0], got[0])
+
+
+if __name__ == "__main__":
+    unittest.main()