[{'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:from sklearn.metrics import make_scorer\n\ndef rmsle(predicted_values, actual_values):\n    #    .\n    predicted_values = np.array(predicted_values)\n    actual_values = np.array(actual_values)\n    \n    #    1   .\n    log_predict = np.log(predicted_values + 1)\n    log_actual = np.log(actual_values + 1)\n    \n    #       .\n    difference = log_predict - log_actual\n    # difference = (log_predict - log_actual) ** 2\n    difference = np.square(difference)\n    \n    #  .\n    mean_difference = difference.mean()\n    \n    #   .\n    score = np.sqrt(mean_difference)\n    \n    return score\n\nrmsle_scorer = make_scorer(rmsle)\nrmsle_scorer'"}, {'role': 'assistant', 'content': "['evaluation', 'prediction']"}]