diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index f478bbd5e80921cfbee3b464bca3a95c609305a0..722221d74aabad5a06f94925bdd790ea115e3d01 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -701,18 +701,18 @@ 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_
+        # 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