diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index 722221d74aabad5a06f94925bdd790ea115e3d01..5194a95b4c3b519ca02977a44c85ab2e0f04af2a 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -701,22 +701,11 @@ class MetaModel():
         # The first column must be kept (For mean calculations)
         nnz_idx = np.nonzero(clf_poly.coef_)[0]
 
-        # if len(nnz_idx) == 0 or nnz_idx[0] != 0:
-        #     nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
-        #     # Remove the zero entries for Bases and PSI if need be
-        #     if sparsity:
-        #         sparse_basis_indices = basis_indices.toarray()[nnz_idx]
-        #     else:
-        #         sparse_basis_indices = basis_indices[nnz_idx]
-        #     sparse_X = X[:, nnz_idx]
-
-        #     # Store the coefficients of the regression model
-        #     clf_poly.fit(sparse_X, y)
-        #     coeffs = clf_poly.coef_
-
         # This is for the case where all outputs are zero, thereby
         # all coefficients are zero
-        nnz_idx = np.nonzero(clf_poly.coef_)[0]
+        if len(nnz_idx) == 0 or nnz_idx[0] != 0:
+            nnz_idx = np.insert(np.nonzero(clf_poly.coef_)[0], 0, 0)
+
         if sparsity:
             sparse_basis_indices = basis_indices.toarray()[nnz_idx]
         else: