Ran for all models

model_selection/test_models.py

@@ -250,13 +250,12 @@ if __name__ == "__main__":
             # Scores df
             scores_df = pd.DataFrame(index=models.keys(), columns=scorings.keys())
             # Create a figure for all models in this group-method
-            fig, axes = plt.subplots(len(models), 2, figsize=(12, 8 * len(models)))
+            fig, axes = plt.subplots(len(models), 2, figsize=(8, 8 * len(models)))
             if len(models) == 1:  # Adjustment if there's only one model (axes indexing issue)
                 axes = [axes]
             # Evaluate each model
             for model_idx, (model_name, model) in enumerate(models.items()):
                 # ----------- TEMPORAL -------------
-                if model_name == "DT" or model_name == "RF":
                 # Train the model (it was just initialized above)
                 model.fit(X_train, y_train)
                 if hasattr(model, "decision_function"):