[{'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:p1 = figure(title="The Performance over the number of neurons\' pruned", tools=TOOLS)\np1.line(ind, ucb1, legend="ucb1", line_color="orange", line_width=2)\np1.line(ind, EpsilonGreedy, legend="Epsilon Greedy", line_color="red", line_width=2)\np1.line(ind, AnnealingEpsilonGreedy, legend="Annealing Epsilon Greedy", line_color="blue", line_width=2)\np1.line(ind, Softmax, legend="Softmax", line_color="green", line_width=2)\np1.line(ind, AnnealingSoftmax, legend="Annealing Softmax", line_color="grey", line_width=2)\np1.line(ind, Exp3, legend="Exp3", line_color="black", line_width=2)\np1.line(ind, Hedge, legend="Hedge", line_color="yellow", line_width=2)\np1.line(ind, ThompsonSampling, legend="Thompson Sampling", line_color="pink", line_width=2)\np1.line(ind, Acc, legend="Accuracy", line_dash=(4, 4), line_color="orange", line_width=2)\np1.title.align = "center"\nshow(p1)'}, {'role': 'assistant', 'content': 'result_visualization'}]