diff --git a/src/grid/GridSearch.cpp b/src/grid/GridSearch.cpp
index 6d8699c..2f79938 100644
--- a/src/grid/GridSearch.cpp
+++ b/src/grid/GridSearch.cpp
@@ -142,6 +142,7 @@ namespace platform {
         auto states = dataset.getStates(); // Get the states of the features Once they are discretized
         double best_fold_score = 0.0;
         int best_idx_combination = -1;
+        bayesnet::Smoothing_t smoothing = bayesnet::Smoothing_t::NONE;
         json best_fold_hyper;
         for (int idx_combination = 0; idx_combination < combinations.size(); ++idx_combination) {
             auto hyperparam_line = combinations[idx_combination];
@@ -167,7 +168,7 @@ namespace platform {
                 hyperparameters.check(valid, dataset_name);
                 clf->setHyperparameters(hyperparameters.get(dataset_name));
                 // Train model
-                clf->fit(X_nested_train, y_nested_train, features, className, states);
+                clf->fit(X_nested_train, y_nested_train, features, className, states, smoothing);
                 // Test model
                 score += clf->score(X_nested_test, y_nested_test);
             }
@@ -186,7 +187,7 @@ namespace platform {
         auto valid = clf->getValidHyperparameters();
         hyperparameters.check(valid, dataset_name);
         clf->setHyperparameters(best_fold_hyper);
-        clf->fit(X_train, y_train, features, className, states);
+        clf->fit(X_train, y_train, features, className, states, smoothing);
         best_fold_score = clf->score(X_test, y_test);
         // Return the result
         result->idx_dataset = task["idx_dataset"].get<int>();
diff --git a/src/main/Experiment.cpp b/src/main/Experiment.cpp
index 3866c97..5e9fab2 100644
--- a/src/main/Experiment.cpp
+++ b/src/main/Experiment.cpp
@@ -194,8 +194,7 @@ namespace platform {
                 //
                 // Train model
                 //
-                clf->setSmoothing(smooth_type);
-                clf->fit(X_train, y_train, features, className, states);
+                clf->fit(X_train, y_train, features, className, states, smooth_type);
                 if (!quiet)
                     showProgress(nfold + 1, getColor(clf->getStatus()), "b");
                 auto clf_notes = clf->getNotes();