[{'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_score_distribution(bags, num_tries=60, size=10000, fit=norm, extremal_fit=gumbel_r):<br>    scores = np.zeros(size)<br>    for i, bag in enumerate(bags):<br>        current_bag_sampler, _ = create_bag_utility_sampler(bag)<br>        scores += current_bag_sampler(size)<br>    score_mean, score_std = np.mean(scores), np.std(scores)<br>    print("Scores: mean = {:0.2f} | std = {:0.2f}".format(score_mean, score_std))<br>    sns.distplot(scores, fit=fit, kde=False)<br>    <br>    plot_extreme_value_distribution(scores, num_tries)<br>    plt.title("Score distribution / submission distribution with {} tries".format(num_tries))<br>    plt.show()<br><br>def plot_extreme_value_distribution(scores, num_tries, size=10000):<br>    samples = np.max(np.random.choice(scores, size=(size, num_tries)), axis=1)<br>    sns.distplot(samples, fit=gumbel_r, kde=False)<br>    expected_score = np.mean(samples)<br>    plt.axvline(expected_score, color=\'r\')<br>    print("Expected score after {} trials: {:0.2f}".format(num_tries, expected_score))<br><br>plot_score_distribution(example_bags)'}, {'role': 'assistant', 'content': "['result_visualization']"}]