diff --git a/stree/Strees.py b/stree/Strees.py
index ab35b4b..7c29a2e 100644
--- a/stree/Strees.py
+++ b/stree/Strees.py
@@ -635,6 +635,7 @@ class Stree(BaseEstimator, ClassifierMixin):
             X = X[~indices_zero, :]
             y = y[~indices_zero]
             sample_weight = sample_weight[~indices_zero]
+        self.depth_ = max(depth, self.depth_)
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
             return Snode(
@@ -652,7 +653,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         clf.fit(Xs, y, sample_weight=sample_weight)
         impurity = self.splitter_.partition_impurity(y)
         node = Snode(clf, X, y, features, impurity, title, sample_weight)
-        self.depth_ = max(depth, self.depth_)
         self.splitter_.partition(X, node, True)
         X_U, X_D = self.splitter_.part(X)
         y_u, y_d = self.splitter_.part(y)
@@ -668,8 +668,14 @@ class Stree(BaseEstimator, ClassifierMixin):
                 title=title + ", <cgaf>",
                 weight=sample_weight,
             )
-        node.set_up(self.train(X_U, y_u, sw_u, depth + 1, title + " - Up"))
-        node.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + " - Down"))
+        node.set_up(
+            self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
+        )
+        node.set_down(
+            self.train(
+                X_D, y_d, sw_d, depth + 1, title + f" - Down({depth+1})"
+            )
+        )
         return node
 
     def _build_predictor(self):
diff --git a/stree/tests/Stree_test.py b/stree/tests/Stree_test.py
index 65afeef..319f60f 100644
--- a/stree/tests/Stree_test.py
+++ b/stree/tests/Stree_test.py
@@ -103,20 +103,20 @@ class Stree_test(unittest.TestCase):
     def test_iterator_and_str(self):
         """Check preorder iterator"""
         expected = [
-            "root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), arr"
-            "ay([750, 750]))",
-            "root - Down, <cgaf> - Leaf class=0 belief= 0.928297 impurity=0.37"
-            "22 counts=(array([0, 1]), array([725,  56]))",
-            "root - Up feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, 1])"
-            ", array([ 25, 694]))",
-            "root - Up - Down feaures=(0, 1, 2) impurity=0.8454 counts=(array("
-            "[0, 1]), array([8, 3]))",
-            "root - Up - Down - Down, <pure> - Leaf class=0 belief= 1.000000 i"
-            "mpurity=0.0000 counts=(array([0]), array([7]))",
-            "root - Up - Down - Up, <cgaf> - Leaf class=1 belief= 0.750000 imp"
-            "urity=0.8113 counts=(array([0, 1]), array([1, 3]))",
-            "root - Up - Up, <cgaf> - Leaf class=1 belief= 0.975989 impurity=0"
-            ".1634 counts=(array([0, 1]), array([ 17, 691]))",
+            "root feaures=(0, 1, 2) impurity=1.0000 counts=(array([0, 1]), "
+            "array([750, 750]))",
+            "root - Down(2), <cgaf> - Leaf class=0 belief= 0.928297 impurity="
+            "0.3722 counts=(array([0, 1]), array([725,  56]))",
+            "root - Up(2) feaures=(0, 1, 2) impurity=0.2178 counts=(array([0, "
+            "1]), array([ 25, 694]))",
+            "root - Up(2) - Down(3) feaures=(0, 1, 2) impurity=0.8454 counts="
+            "(array([0, 1]), array([8, 3]))",
+            "root - Up(2) - Down(3) - Down(4), <pure> - Leaf class=0 belief= "
+            "1.000000 impurity=0.0000 counts=(array([0]), array([7]))",
+            "root - Up(2) - Down(3) - Up(4), <cgaf> - Leaf class=1 belief= "
+            "0.750000 impurity=0.8113 counts=(array([0, 1]), array([1, 3]))",
+            "root - Up(2) - Up(3), <cgaf> - Leaf class=1 belief= 0.975989 "
+            "impurity=0.1634 counts=(array([0, 1]), array([ 17, 691]))",
         ]
         computed = []
         expected_string = ""
@@ -439,3 +439,18 @@ class Stree_test(unittest.TestCase):
         self.assertEqual(model1.score(X, y), 1)
         self.assertAlmostEqual(model2.score(X, y), 0.66666667)
         self.assertEqual(model2.score(X, y, w), 1)
+
+    def test_depth(self):
+        X, y = load_dataset(
+            random_state=self._random_state,
+            n_classes=3,
+            n_features=5,
+            n_samples=1500,
+        )
+        clf = Stree(random_state=self._random_state)
+        clf.fit(X, y)
+        self.assertEqual(6, clf.depth_)
+        X, y = load_wine(return_X_y=True)
+        clf = Stree(random_state=self._random_state)
+        clf.fit(X, y)
+        self.assertEqual(7, clf.depth_)