[SYCL] Fix for sycl support with sklearn estimators (#10806)

---------

Co-authored-by: Dmitry Razdoburdin <>

tests/python-sycl/test_sycl_with_sklearn.py

@@ -0,0 +1,37 @@
+import xgboost as xgb
+import pytest
+import sys
+import numpy as np
+
+from xgboost import testing as tm
+
+sys.path.append("tests/python")
+import test_with_sklearn as twskl  # noqa
+
+pytestmark = pytest.mark.skipif(**tm.no_sklearn())
+
+rng = np.random.RandomState(1994)
+
+
+def test_sycl_binary_classification():
+    from sklearn.datasets import load_digits
+    from sklearn.model_selection import KFold
+
+    digits = load_digits(n_class=2)
+    y = digits["target"]
+    X = digits["data"]
+    kf = KFold(n_splits=2, shuffle=True, random_state=rng)
+    for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
+        for train_index, test_index in kf.split(X, y):
+            xgb_model = cls(random_state=42, device="sycl", n_estimators=4).fit(
+                X[train_index], y[train_index]
+            )
+            preds = xgb_model.predict(X[test_index])
+            labels = y[test_index]
+            err = sum(
+                1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]
+            ) / float(len(preds))
+            print(preds)
+            print(labels)
+            print(err)
+            assert err < 0.1