[{'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:# FUNCTION FOR COMPARING DIFFERENT REGRESSORS\ndef algorithim_boxplot_comparison(\n    X, y, algo_list=[], random_state=3, scoring="r2", n_splits=10\n):\n    \n    results = []\n    names = []\n    for algo_name, algo_model in algo_list:\n        kfold = model_selection.KFold(\n            shuffle=True, n_splits=n_splits, random_state=random_state\n        )\n        cv_results = model_selection.cross_val_score(\n            algo_model, X, y, cv=kfold, scoring=scoring\n        )\n        results.append(cv_results)\n        names.append(algo_name)\n        msg = "%s: %s : (%f) %s : (%f) %s : (%f)" % (\n            algo_name,\n            "median",\n            np.median(cv_results),\n            "mean",\n            np.mean(cv_results),\n            "variance",\n            cv_results.var(ddof=1),\n        )\n        print(msg)\n    # boxplot algorithm comparison\n    fig = plt.figure()\n    fig.suptitle("Algorithm Comparison")\n    ax = fig.add_subplot(111)\n    plt.boxplot(results)\n    ax.set_xticklabels(names)\n    plt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]