diff --git a/rdagent/scenarios/kaggle/developer/coder.py b/rdagent/scenarios/kaggle/developer/coder.py
index 1aa0ead1..3c02f552 100644
--- a/rdagent/scenarios/kaggle/developer/coder.py
+++ b/rdagent/scenarios/kaggle/developer/coder.py
@@ -29,7 +29,7 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     {% if feature_index_list is not none %}
     X = X.loc[:, X.columns.levels[0][{{feature_index_list}}].tolist()]
     {% endif %}
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
 """
 
diff --git a/rdagent/scenarios/kaggle/experiment/covid19-global-forecasting-week-1_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/covid19-global-forecasting-week-1_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/covid19-global-forecasting-week-1_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/covid19-global-forecasting-week-1_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_nn.py b/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_nn.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_nn.py
+++ b/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_nn.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/digit-recognizer_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/feedback-prize-english-language-learning_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_nn.py b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_nn.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_nn.py
+++ b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_nn.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/forest-cover-type-prediction_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/optiver-realized-volatility-prediction_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s3e11_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s3e26_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s4e8_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/playground-series-s4e9_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/sf-crime_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py
+++ b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_nn.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py
+++ b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_randomforest.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/spaceship-titanic_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X
diff --git a/rdagent/scenarios/kaggle/experiment/statoil-iceberg-classifier-challenge_template/model/select_xgboost.py b/rdagent/scenarios/kaggle/experiment/statoil-iceberg-classifier-challenge_template/model/select_xgboost.py
index d2a15dee..f230f130 100644
--- a/rdagent/scenarios/kaggle/experiment/statoil-iceberg-classifier-challenge_template/model/select_xgboost.py
+++ b/rdagent/scenarios/kaggle/experiment/statoil-iceberg-classifier-challenge_template/model/select_xgboost.py
@@ -8,5 +8,5 @@ def select(X: pd.DataFrame) -> pd.DataFrame:
     # For now, we assume all features are relevant. This can be expanded to feature selection logic.
     if X.columns.nlevels == 1:
         return X
-    X.columns = ["_".join(str(col)).strip() for col in X.columns.values]
+    X.columns = ["_".join(str(i) for i in col).strip() for col in X.columns.values]
     return X