diff --git a/src/hidimstat/base_perturbation.py b/src/hidimstat/base_perturbation.py
index b025659e2..c8f755280 100644
--- a/src/hidimstat/base_perturbation.py
+++ b/src/hidimstat/base_perturbation.py
@@ -120,11 +120,9 @@ def predict(self, X):
         # Parallelize the computation of the importance scores for each group
         out_list = Parallel(n_jobs=self.n_jobs)(
             delayed(self._joblib_predict_one_group)(
-                X_, group_id, group_key, random_state=child_state
-            )
-            for group_id, (group_key, child_state) in enumerate(
-                zip(self.groups.keys(), rng.spawn(self.n_groups))
+                X_, group_id, random_state=child_state
             )
+            for group_id, child_state in enumerate(rng.spawn(self.n_groups))
         )
         return np.stack(out_list, axis=0)
 
@@ -241,7 +239,7 @@ def _check_fit(self, X):
                 f"{number_unique_feature_in_groups}"
             )
 
-    def _joblib_predict_one_group(self, X, group_id, group_key, random_state=None):
+    def _joblib_predict_one_group(self, X, group_id, random_state=None):
         """
         Compute the predictions after perturbation of the data for a given
         group of variables. This function is parallelized.
@@ -252,8 +250,6 @@ def _joblib_predict_one_group(self, X, group_id, group_key, random_state=None):
             The input samples.
         group_id: int
             The index of the group of variables.
-        group_key: str, int
-            The key of the group of variables. (parameter use for debugging)
         random_state:
             The random state to use for sampling.
         """
diff --git a/src/hidimstat/leave_one_covariate_out.py b/src/hidimstat/leave_one_covariate_out.py
index a199c7928..94b993132 100644
--- a/src/hidimstat/leave_one_covariate_out.py
+++ b/src/hidimstat/leave_one_covariate_out.py
@@ -93,7 +93,7 @@ def _joblib_fit_one_group(self, estimator, X, y, key_groups):
         estimator.fit(X_minus_j, y)
         return estimator
 
-    def _joblib_predict_one_group(self, X, group_id, key_groups, random_state=None):
+    def _joblib_predict_one_group(self, X, group_id, random_state=None):
         """Predict the target variable after removing a group of covariates.
         Used in parallel."""
         X_minus_j = np.delete(X, self._groups_ids[group_id], axis=1)