From 56db791e7e9b75cdb3c3df84d70e363df70c1867 Mon Sep 17 00:00:00 2001 From: Joaquin Torres Bravo Date: Wed, 5 Jun 2024 16:30:04 +0200 Subject: [PATCH] Realized its better to tune based on f1 to achieve good recall --- model_selection/hyperparam_tuning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py index 0892526..3ebeeaa 100644 --- a/model_selection/hyperparam_tuning.py +++ b/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_ -- 2.24.1