diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py
index 3fb508ddb693dd72c0f50423d96b76c3244b3b7f..0892526c0d6a8a3845c5dda366e4753f582ed0f3 100644
--- a/model_selection/hyperparam_tuning.py
+++ b/model_selection/hyperparam_tuning.py
@@ -147,8 +147,6 @@ if __name__ == "__main__":
             hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Best Parameters'])
             for model_name, model in models.items():
                 print(f"{group}-{method_names[j]}-{model_name}")
-                if model_name != 'XGB':
-                    continue
                 # Find optimal hyperparams for curr model
                 params = hyperparameters[model_name]
                 search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=10, scoring='precision')