diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py
index a58e63edb..4f35773b7 100644
--- a/fedot_ind/api/main.py
+++ b/fedot_ind/api/main.py
@@ -232,10 +232,10 @@ def predict_proba(self,
         Args:
             predict_mode: ``default='default'``. Defines the mode of prediction. Could be 'default' or 'probs'.
             predict_data: tuple with test_features and test_target
+            calibrate_probs: ``default=False``. If True, calibrate probabilities
 
         Returns:
             the array with prediction probabilities
-            :param calibrate_probs:
 
         """
         self.predict_data = self._process_input_data(predict_data)
@@ -262,7 +262,8 @@ def finetune(self,
 
         train_data = self._process_input_data(train_data) if \
             not self.api_controller.condition_check.input_data_is_fedot_type(train_data) else train_data
-        tuning_params = ApiConverter.tuning_params_is_none(tuning_params)
+        if tuning_params is None:
+            tuning_params = ApiConverter.tuning_params_is_none(tuning_params)
         tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config_dict['problem']]
 
         for tuner_name, tuner_type in FEDOT_TUNER_STRATEGY.items():
diff --git a/tests/unit/api/main/test_api_main.py b/tests/unit/api/main/test_api_main.py
index 13561ff60..8ad2b4412 100644
--- a/tests/unit/api/main/test_api_main.py
+++ b/tests/unit/api/main/test_api_main.py
@@ -140,7 +140,7 @@ def test_finetune(fedot_industrial_classification):
     industrial = fedot_industrial_classification
     data = univariate_clf_data()
     industrial.fit(data)
-    industrial.finetune(data)
+    industrial.finetune(train_data=data, tuning_params={'tuning_timeout': 0.1})
     assert industrial.solver is not None