diff --git a/modnet/models/vanilla.py b/modnet/models/vanilla.py
index 6eb8b4ff..bc690298 100644
--- a/modnet/models/vanilla.py
+++ b/modnet/models/vanilla.py
@@ -3,27 +3,25 @@
 
 """
 
+import multiprocessing
+import warnings
 from collections import defaultdict
-from typing import List, Tuple, Dict, Optional, Callable, Any, Union
-
 from pathlib import Path
-import multiprocessing
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-import pandas as pd
 import numpy as np
-import warnings
-from sklearn.preprocessing import StandardScaler, MinMaxScaler
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
+import pandas as pd
+import tensorflow as tf
+import tqdm
 from sklearn.impute import SimpleImputer
+from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
+from sklearn.model_selection import train_test_split
 from sklearn.pipeline import Pipeline
-import tensorflow as tf
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
 
+from modnet import __version__
 from modnet.preprocessing import MODData
 from modnet.utils import LOG
-from modnet import __version__
-
-import tqdm
 
 __all__ = ("MODNetModel",)
 
@@ -532,9 +530,10 @@ def fit_preset(
 
         """
 
-        from modnet.matbench.benchmark import matbench_kfold_splits
         import os
 
+        from modnet.matbench.benchmark import matbench_kfold_splits
+
         os.environ["TF_CPP_MIN_LOG_LEVEL"] = (
             "2"  # many models will be fitted => reduce output
         )
@@ -1358,7 +1357,7 @@ def fit(
         history = self.model.fit(**fit_params)
         self.history = history.history
 
-    def predict(self, test_data: MODData, return_prob=False) -> pd.DataFrame:
+    def predict(self, test_data: MODData, return_prob=False, **kwargs) -> pd.DataFrame:
         """Predict the target values for the passed MODData.
 
         Parameters:
@@ -1372,6 +1371,7 @@ class OR only return the most probable class.
 
 
         """
+        # kwargs added for compatibility of this DeprecatedMODNetModel with ensemble (e.g. remap_out_of_bounds could be given but ignored here)
         # prevents Nan predictions if some features are inf
         x = (
             test_data.get_featurized_df()
@@ -1381,6 +1381,9 @@ class OR only return the most probable class.
 
         # Scale and impute input features:
         if self._scale_impute is not None:
+            self._scale_impute.named_steps["scaler"].clip = (
+                False  # deprecated compatibility
+            )
             x = self._scale_impute.transform(x)
 
         p = np.array(self.model.predict(x))