diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 9f04e0ca4..16a3bc3c7 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -24,8 +24,6 @@ from freqtrade.strategy import merge_informative_pair
 from freqtrade.strategy.interface import IStrategy
 
 
-pd.set_option("future.no_silent_downcasting", True)
-
 SECONDS_IN_DAY = 86400
 SECONDS_IN_HOUR = 3600
 
@@ -239,16 +237,14 @@ class FreqaiDataKitchen:
         filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
 
         drop_index = pd.isnull(filtered_df).any(axis=1)  # get the rows that have NaNs,
-        drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects(copy=False)
+        drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects()
         if training_filter:
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
             labels = unfiltered_df.filter(label_list or [], axis=1)
             drop_index_labels = pd.isnull(labels).any(axis=1)
-            drop_index_labels = (
-                drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False)
-            )
+            drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0).infer_objects()
             dates = unfiltered_df["date"]
             filtered_df = filtered_df[
                 (drop_index == 0) & (drop_index_labels == 0)