[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:importances = rfr_val.feature_importances_\nstd = np.std([rfr_val.feature_importances_ for tree in rfr_val.estimators_],\n             axis=0)\nindices = np.argsort(importances)\npalette1 = itertools.cycle(sns.color_palette())\n# Store the feature ranking\nfeatures_ranked=[]\nfor f in range(X_train.shape[1]):\n    features_ranked.append(X_train.columns[indices[f]])\n# Plot the feature importances of the forest\n\nplt.figure(figsize=(10,15))\nplt.title("Feature importances")\nplt.barh(range(X_train.shape[1]), importances[indices],\n            color=[next(palette1)], align="center")\nplt.yticks(range(X_train.shape[1]), features_ranked)\nplt.ylabel(\'Features\')\nplt.ylim([-1, X_train.shape[1]])\nplt.show()\n\n\''}, {'role': 'assistant', 'content': 'result_visualization'}]