[{'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:plt.figure(figsize=(8, 2))\nplt.title("Time series cross-validation")\naxes = plt.gca()\naxes.set_frame_on(False)\nn_folds = 5\nn_samples = 25\nn_samples_per_fold = n_samples / float(n_folds)\nfor i in range(n_folds + 1):\ntherange = range(max(i - 1, 0), n_folds)\ncolors = ["w"] * len(therange)\nif i != 0:\ncolors[0] = "grey"\nbars = plt.barh(bottom=therange, width=[n_samples_per_fold - 0.1] * len(therange),\nleft=i * n_samples_per_fold, height=.6, color=colors, hatch="//", edgecolor=\'k\')\naxes.invert_yaxis()\naxes.set_xlim(0, n_samples + 1 + n_samples_per_fold)\nplt.ylabel("CV iterations")\nplt.xlabel("Data points")\nplt.yticks(np.arange(n_folds) + .3, ["Split %d" % x for x in range(1, n_folds + 1)])\nplt.legend([bars[0], bars[-1]], [\'Training data\', \'Test data\'], loc=(1.05, 0.4), frameon=False);\nplt.tight_layout()'}, {'role': 'assistant', 'content': 'result_visualization'}]