diff --git a/stree/Strees.py b/stree/Strees.py
index 0b02e39..e5ce526 100644
--- a/stree/Strees.py
+++ b/stree/Strees.py
@@ -68,14 +68,14 @@ class Snode:
         if len(classes) > 1:
             max_card = max(card)
             min_card = min(card)
-            try:
-                self._belief = max_card / (max_card + min_card)
-            except ZeroDivisionError:
-                self._belief = 0.0
             self._class = classes[card == max_card][0]
+            self._belief = max_card / (max_card + min_card)
         else:
             self._belief = 1
-            self._class = classes[0]
+            try:
+                self._class = classes[0]
+            except IndexError:
+                self._class = None
 
     def __str__(self) -> str:
         if self.is_leaf():
@@ -182,6 +182,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         if res.ndim == 1:
             return np.expand_dims(res, 1)
         elif res.shape[1] > 1:
+            # remove multiclass info
             res = np.delete(res, slice(1, res.shape[1]), axis=1)
         return res
 
@@ -216,8 +217,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         :rtype: Stree
         """
         # Check parameters are Ok.
-        if type(y).__name__ == "np.ndarray":
-            y = y.ravel()
         if self.C < 0:
             raise ValueError(
                 f"Penalty term must be positive... got (C={self.C:f})"
@@ -463,7 +462,8 @@ class Stree(BaseEstimator, ClassifierMixin):
         """
         # sklearn check
         check_is_fitted(self)
-
+        check_classification_targets(y)
+        X, y = check_X_y(X, y)
         y_pred = self.predict(X).reshape(y.shape)
         # Compute accuracy for each possible representation
         y_type, y_true, y_pred = _check_targets(y, y_pred)
diff --git a/stree/tests/Strees_test.py b/stree/tests/Strees_test.py
index fe3cd1f..8bda05b 100644
--- a/stree/tests/Strees_test.py
+++ b/stree/tests/Strees_test.py
@@ -256,6 +256,15 @@ class Stree_test(unittest.TestCase):
         self.assertIsNone(tcl_nosplit.tree_.get_down())
         self.assertIsNone(tcl_nosplit.tree_.get_up())
 
+    def test_muticlass_dataset(self):
+        for kernel in self._kernels:
+            clf = Stree(kernel=kernel, random_state=self._random_state)
+            px = [[1, 2], [3, 4], [5, 6]]
+            py = [1, 2, 3]
+            clf.fit(px, py)
+            self.assertEqual(1.0, clf.score(px, py))
+            self.assertListEqual([1, 2, 3], clf.predict(px).tolist())
+
 
 class Snode_test(unittest.TestCase):
     def __init__(self, *args, **kwargs):
@@ -324,3 +333,8 @@ class Snode_test(unittest.TestCase):
         test.make_predictor()
         self.assertIsNone(test._class)
         self.assertEqual(0, test._belief)
+
+    def test_make_predictor_on_leaf_bogus_data(self):
+        test = Snode(None, [1, 2, 3, 4], [], "test")
+        test.make_predictor()
+        self.assertIsNone(test._class)