[{'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:fig = plt.figure(figsize=(10, 6), dpi=80)\nax = fig.add_subplot(111)\nN = len(Exp3)\nAcc = [Accuracy[0] for col in range(N)]\nind = np.arange(N)                # the x locations for the groups\nplt.plot(ind , Exp3, color="black", linewidth=2.5, linestyle="-", label="Exp3")\nplt.plot(ind , Hedge, color="green", linewidth=2.5, linestyle="-", label="Hedge")\nplt.plot(ind , Acc, color="pink", linewidth=2.5, linestyle="-", label="Accuracy before Pruning")\nplt.legend(loc = 3)\nplt.axis([0, 25, 0, 1])\nplt.xlabel(\'Number of pruned Neurons\')\nplt.ylabel(\'The Accuracy\')\nplt.grid(True)\nplt.show()'}, {'role': 'assistant', 'content': 'result_visualization'}]