diff --git a/qlib/contrib/model/catboost_model.py b/qlib/contrib/model/catboost_model.py
index d53a6db41..e487a6d1e 100644
--- a/qlib/contrib/model/catboost_model.py
+++ b/qlib/contrib/model/catboost_model.py
@@ -61,7 +61,7 @@ class CatBoostModel(Model):
         if self.model is None:
             raise ValueError("model is not fitted yet!")
         x_test = dataset.prepare("test", col_set="feature")
-        return pd.Series(self.model.predict(np.squeeze(x_test.values)), index=x_test.index)
+        return pd.Series(self.model.predict(x_test.values), index=x_test.index)
 
 
 if __name__ == "__main__":
diff --git a/qlib/contrib/model/gbdt.py b/qlib/contrib/model/gbdt.py
index 58b76c355..995a02696 100644
--- a/qlib/contrib/model/gbdt.py
+++ b/qlib/contrib/model/gbdt.py
@@ -16,7 +16,7 @@ class LGBModel(ModelFT):
     def __init__(self, loss="mse", **kwargs):
         if loss not in {"mse", "binary"}:
             raise NotImplementedError
-        self.params = {"objective": loss}
+        self.params = {"objective": loss, 'verbosity': -1}
         self.params.update(kwargs)
         self.model = None
 
@@ -65,7 +65,7 @@ class LGBModel(ModelFT):
         if self.model is None:
             raise ValueError("model is not fitted yet!")
         x_test = dataset.prepare("test", col_set="feature", data_key=DataHandlerLP.DK_I)
-        return pd.Series(self.model.predict(np.squeeze(x_test.values)), index=x_test.index)
+        return pd.Series(self.model.predict(x_test.values), index=x_test.index)
 
     def finetune(self, dataset: DatasetH, num_boost_round=10, verbose_eval=20):
         """
diff --git a/qlib/contrib/model/xgboost.py b/qlib/contrib/model/xgboost.py
index f1208eb93..e0691ba16 100755
--- a/qlib/contrib/model/xgboost.py
+++ b/qlib/contrib/model/xgboost.py
@@ -61,4 +61,4 @@ class XGBModel(Model):
         if self.model is None:
             raise ValueError("model is not fitted yet!")
         x_test = dataset.prepare("test", col_set="feature")
-        return pd.Series(self.model.predict(xgb.DMatrix(np.squeeze(x_test.values))), index=x_test.index)
+        return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index=x_test.index)