[{'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"):\n"""Plots a confusion matrix for each subject"""\nimport matplotlib.pyplot as plt\nimport math\nplt.figure()\nsubjects = len(cm)\nroot_subjects = math.sqrt(subjects)\ncols = math.ceil(root_subjects)\nrows = math.ceil(subjects/cols)\nclasses = cm[0].shape[0]\nfor subject in range(subjects):\nplt.subplot(rows, cols, subject+1)\nplt.imshow(cm[subject], interpolation=\'nearest\', cmap=plt.cm.bone)\nplt.xticks(np.arange(classes), range(1,classes+1))\nplt.yticks(np.arange(classes), range(1,classes+1))\ncbar = plt.colorbar(ticks=[0.0,1.0], shrink=0.6)\ncbar.set_clim(0.0, 1.0)\nplt.xlabel("Predicted")\nplt.ylabel("True label")\nplt.title("{0:d}".format(subject + 1))\nplt.suptitle(title)\nplt.tight_layout()\nplt.show()'}, {'role': 'assistant', 'content': 'evaluation'}]