diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 127193a35..ecdb2e109 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -371,17 +371,6 @@ class FreqaiDataKitchen:
 
         return df
 
-    def add_noise_to_training_features(self) -> None:
-        """
-        Add noise to train features to reduce the risk of overfitting.
-        """
-        mu = 0  # no shift
-        sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
-        compute_df = self.data_dictionary['train_features']
-        noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
-        self.data_dictionary['train_features'] += noise
-        return
-
     def find_features(self, dataframe: DataFrame) -> None:
         """
         Find features in the strategy provided dataframe
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index ffe0ee8c3..632266b00 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -510,12 +510,12 @@ class IFreqaiModel(ABC):
     def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None:
         ft_params = self.freqai_info["feature_parameters"]
         dk.feature_pipeline = Pipeline([
-            ('const', ds.DataSieveVarianceThreshold(threshold=0)),
+            ('const', ds.VarianceThreshold(threshold=0)),
             ('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))))
             ])
 
         if ft_params.get("principal_component_analysis", False):
-            dk.feature_pipeline.append(('pca', ds.DataSievePCA()))
+            dk.feature_pipeline.append(('pca', ds.PCA()))
             dk.feature_pipeline.append(('post-pca-scaler',
                                         SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
 
@@ -529,14 +529,15 @@ class IFreqaiModel(ABC):
             dk.feature_pipeline.append(('di', ds.DissimilarityIndex(di_threshold=di)))
 
         if ft_params.get("use_DBSCAN_to_remove_outliers", False):
-            dk.feature_pipeline.append(('dbscan', ds.DataSieveDBSCAN()))
+            dk.feature_pipeline.append(('dbscan', ds.DBSCAN()))
+
+        sigma = self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0)
+        if sigma:
+            dk.feature_pipeline.append(('noise', ds.Noise(sigma=sigma)))
 
         dk.feature_pipeline.fitparams = dk.feature_pipeline._validate_fitparams(
             {}, dk.feature_pipeline.steps)
 
-        # if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
-        #     dk.pipeline.extend(('noise', ds.Noise()))
-
     def define_label_pipeline(self, dk: FreqaiDataKitchen) -> None:
 
         dk.label_pipeline = Pipeline([
diff --git a/requirements-freqai.txt b/requirements-freqai.txt
index 748950e24..a515ba2b5 100644
--- a/requirements-freqai.txt
+++ b/requirements-freqai.txt
@@ -10,4 +10,4 @@ catboost==1.2; 'arm' not in platform_machine and (sys_platform != 'darwin' or py
 lightgbm==3.3.5
 xgboost==1.7.5
 tensorboard==2.13.0
-datasieve==0.1.1
+datasieve==0.1.2
diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py
index 90959ec2c..fec9de724 100644
--- a/tests/freqai/test_freqai_interface.py
+++ b/tests/freqai/test_freqai_interface.py
@@ -37,21 +37,22 @@ def can_run_model(model: str) -> None:
         pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")
 
 
-@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer', [
-    ('LightGBMRegressor', True, False, True, True, False, 0),
-    ('XGBoostRegressor', False, True, False, True, False, 10),
-    ('XGBoostRFRegressor', False, False, False, True, False, 0),
-    ('CatboostRegressor', False, False, False, True, True, 0),
-    ('PyTorchMLPRegressor', False, False, False, False, False, 0),
-    ('PyTorchTransformerRegressor', False, False, False, False, False, 0),
-    ('ReinforcementLearner', False, True, False, True, False, 0),
-    ('ReinforcementLearner_multiproc', False, False, False, True, False, 0),
-    ('ReinforcementLearner_test_3ac', False, False, False, False, False, 0),
-    ('ReinforcementLearner_test_3ac', False, False, False, True, False, 0),
-    ('ReinforcementLearner_test_4ac', False, False, False, True, False, 0),
+@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer, noise', [
+    ('LightGBMRegressor', True, False, True, True, False, 0, 0),
+    ('XGBoostRegressor', False, True, False, True, False, 10, 0.05),
+    ('XGBoostRFRegressor', False, False, False, True, False, 0, 0),
+    ('CatboostRegressor', False, False, False, True, True, 0, 0),
+    ('PyTorchMLPRegressor', False, False, False, False, False, 0, 0),
+    ('PyTorchTransformerRegressor', False, False, False, False, False, 0, 0),
+    ('ReinforcementLearner', False, True, False, True, False, 0, 0),
+    ('ReinforcementLearner_multiproc', False, False, False, True, False, 0, 0),
+    ('ReinforcementLearner_test_3ac', False, False, False, False, False, 0, 0),
+    ('ReinforcementLearner_test_3ac', False, False, False, True, False, 0, 0),
+    ('ReinforcementLearner_test_4ac', False, False, False, True, False, 0, 0),
     ])
 def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
-                                               dbscan, float32, can_short, shuffle, buffer):
+                                               dbscan, float32, can_short, shuffle,
+                                               buffer, noise):
 
     can_run_model(model)
 
@@ -68,6 +69,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     freqai_conf.update({"reduce_df_footprint": float32})
     freqai_conf['freqai']['feature_parameters'].update({"shuffle_after_split": shuffle})
     freqai_conf['freqai']['feature_parameters'].update({"buffer_train_data_candles": buffer})
+    freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": noise})
 
     if 'ReinforcementLearner' in model:
         model_save_ext = 'zip'