diff --git a/docs/examples/example_survival.ipynb b/docs/examples/example_survival.ipynb
index 8c0f5d7..f84a813 100644
--- a/docs/examples/example_survival.ipynb
+++ b/docs/examples/example_survival.ipynb
@@ -100,7 +100,8 @@
     "        # X contains \"censored\" column\n",
     "        features = set(X.columns) - {\"censored\"}\n",
     "        dtrain = xgb.DMatrix(X[list(features)], enable_categorical=True)\n",
-    "        if set(np.unique(X[\"censored\"])) != {0, 1}:\n",
+    "\n",
+    "        if (set(np.unique(X[\"censored\"])) - {0, 1}) != set():\n",
     "            raise ValueError(\"censored column should be binary.\")\n",
     "\n",
     "        y_upper_bound = np.where(X[\"censored\"], +np.inf, y)\n",
@@ -122,7 +123,7 @@
     "        check_is_fitted(self)\n",
     "        # X contains \"censored\" column\n",
     "        features = set(X.columns) - {\"censored\"}\n",
-    "        if set(np.unique(X[\"censored\"])) != {0, 1}:\n",
+    "        if (set(np.unique(X[\"censored\"])) - {0, 1}) != set():\n",
     "            raise ValueError(\"censored column should be binary.\")\n",
     "        dtest = xgb.DMatrix(X[list(features)], enable_categorical=True)\n",
     "        return self.bst_.predict(dtest)"