diff --git a/examples/advanced/automl/h2o_example.py b/examples/advanced/automl/h2o_example.py
index f4a0a9d3f4..b16712734a 100644
--- a/examples/advanced/automl/h2o_example.py
+++ b/examples/advanced/automl/h2o_example.py
@@ -73,7 +73,7 @@ def h2o_regression_pipeline_evaluation():
     train_data, test_data = train_test_data_setup(data)
 
     pipeline.fit(input_data=train_data)
-    results = pipeline.predict(input_data=test_data)
+    _ = pipeline.predict(input_data=test_data)
     _, rmse_on_test = get_rmse_value(pipeline, train_data, test_data)
     print(f"RMSE {rmse_on_test}")
 
diff --git a/fedot/core/optimisers/objective/data_source_splitter.py b/fedot/core/optimisers/objective/data_source_splitter.py
index cd36ad2c9c..e3cd1160a2 100644
--- a/fedot/core/optimisers/objective/data_source_splitter.py
+++ b/fedot/core/optimisers/objective/data_source_splitter.py
@@ -36,7 +36,7 @@ def __init__(self,
                  cv_folds: Optional[int] = None,
                  validation_blocks: Optional[int] = None,
                  split_ratio: Optional[float] = None,
-                 shuffle: bool = True,
+                 shuffle: bool = False,
                  stratify: bool = True,
                  random_seed: int = 42):
         self.cv_folds = cv_folds
@@ -152,7 +152,6 @@ def _propose_cv_folds_and_validation_blocks(self, data):
                                f" ({test_shape}) defined by split ratio."
                                f" Split ratio is changed to {self.split_ratio}."))
             test_share = 1 - self.split_ratio
-            self.split_ratio = self.split_ratio
         else:
             test_share = 1 / (self.cv_folds + 1)
         self.validation_blocks = int(data_shape * test_share // forecast_length)
diff --git a/test/unit/data/test_data_split.py b/test/unit/data/test_data_split.py
index 712173452a..8415ecfcbf 100644
--- a/test/unit/data/test_data_split.py
+++ b/test/unit/data/test_data_split.py
@@ -203,7 +203,8 @@ def test_multivariate_time_series_splitting_correct():
 
 
 @pytest.mark.parametrize(('datas_funs', 'cv_folds', 'shuffle', 'stratify'),
-                         [# classification + stratify + shuffle + cv_folds
+                         [
+                          # classification + stratify + shuffle + cv_folds
                           ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, True),
                           # classification + shuffle + cv_folds
                           ([partial(get_tabular_classification_data, 100, 5)] * 3, 4, True, False),