[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def plot_fact_vs_forecast(y_true, y_hat, position, horizon):\n"""\nVisualizes `y_hat` against `y_true`\nfor a time interval of length `horizon`\nthat corresponds to `position`.\n@type y_true: pandas.Series\n@type y_hat: pandas.Series\n@type position: int\n@type horizon: int\n@rtype: NoneType\n"""\nfig = plt.figure(figsize=(15, 6))\nax = fig.add_subplot(111)\nax.set_title(\'Forecast-vs-fact for steps from {} to {}\'.format(\nposition, position + horizon))\nax.set_xticklabels([])\ny_true.plot(ax=ax, marker=\'o\')\npd.Series(y_hat, index=y_true.index).plot(ax=ax, c=\'red\', marker=\'o\')'}, {'role': 'assistant', 'content': "['data_exploration', 'evaluation', 'result_visualization']"}]