[{'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:def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\nplt.imshow(cm, interpolation='nearest', cmap=cmap)\nplt.title(title)\nplt.colorbar()\ntick_marks = np.arange(len(iris.target_names))\nplt.xticks(tick_marks, iris.target_names, rotation=45)\nplt.yticks(tick_marks, iris.target_names)\nplt.tight_layout()\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\ncm = confusion_matrix(y_test, predictions)\nnp.set_printoptions(precision=2)\nprint('Confusion matrix, without normalization')\nprint(cm)\nplt.figure()\nplot_confusion_matrix(cm)\ncm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\nprint('Normalized confusion matrix')\nprint(cm_normalized)\nplt.figure()\nplot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')\nplt.show()"}, {'role': 'assistant', 'content': 'result_visualization'}]