Realized its better to tune based on f1 to achieve good recall

model_selection/hyperparam_tuning.py

@@ -149,7 +149,7 @@ if __name__ == "__main__":
                 print(f"{group}-{method_names[j]}-{model_name}")
                 # Find optimal hyperparams for curr model
                 params = hyperparameters[model_name]
-                search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=10, scoring='precision')
+                search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=10, scoring='f1')
                 search.fit(X,y)
                 # Keep optimal parameters
                 best_params = search.best_params_