diff --git a/custom_loss/custom_loss_and_metric_tutorial.ipynb b/custom_loss/custom_loss_and_metric_tutorial.ipynb
index cb5c005..6982f8e 100644
--- a/custom_loss/custom_loss_and_metric_tutorial.ipynb
+++ b/custom_loss/custom_loss_and_metric_tutorial.ipynb
@@ -280,6 +280,7 @@
     "                            learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                            leaf_estimation_iterations=1, leaf_estimation_method='Gradient')\n",
     "model1.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model1.predict(X_test)"
    ]
   },
   {
@@ -323,6 +324,7 @@
     "                            learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                            leaf_estimation_iterations=1, leaf_estimation_method='Gradient')\n",
     "model2.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model2.predict(X_test)"
    ]
   },
   {
@@ -455,6 +457,7 @@
     "                           learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                           leaf_estimation_iterations=1, leaf_estimation_method='Gradient')\n",
     "model1.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model1.predict(X_test)"
    ]
   },
   {
@@ -498,6 +501,7 @@
     "                           learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                           leaf_estimation_iterations=1, leaf_estimation_method='Gradient')\n",
     "model2.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model2.predict(X_test)"
    ]
   },
   {
@@ -652,6 +656,7 @@
     "                           learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                           leaf_estimation_iterations=1, leaf_estimation_method='Newton', classes_count=5)\n",
     "model1.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model1.predict(X_test)"
    ]
   },
   {
@@ -695,6 +700,7 @@
     "                           learning_rate=0.03, bootstrap_type='Bayesian', boost_from_average=False,\n",
     "                           leaf_estimation_iterations=1, leaf_estimation_method='Newton', classes_count=5)\n",
     "model2.fit(X_train, y_train, eval_set=(X_test, y_test))"
+    "model2.predict(X_test)"
    ]
   }
  ],