[{'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:import matplotlib.pyplot as plt\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\n\ndef plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n    """\n    Generate a simple plot of the test and training learning curve.\n\n    Parameters\n    ----------\n    estimator : object type that implements the "fit" and "predict" methods\n        An object of that type which is cloned for each validation.\n\n    title : string\n        Title for the chart.\n\n    X : array-like, shape (n_samples, n_features)\n        Training vector, where n_samples is the number of samples and\n        n_features is the number of features.\n\n    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n        Target relative to X for classification or regression;\n        None for unsupervised learning.\n\n    ylim : tuple, shape (ymin, ymax), optional\n        Defines minimum and maximum yvalues plotted.\n\n    cv : int, cross-validation generator or an iterable, optional\n        Determines the cross-validation splitting strategy.\n        Possible inputs for cv are:\n          - None, to use the default 3-fold cross-validation,\n          - integer, to specify the number of folds.\n          - :term:`CV splitter`,\n          - An iterable yielding (train, test) splits as arrays of indices.\n\n        For integer/None inputs, if ``y`` is binary or multiclass,\n        :class:`StratifiedKFold` used. If the estimator is not a classifier\n        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n\n        Refer :ref:`User Guide <cross_validation>` for the various\n        cross-validators that can be used here.\n\n    n_jobs : int or None, optional (default=None)\n        Number of jobs to run in parallel.\n        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n        for more details.\n\n    train_sizes : array-like, shape (n_ticks,), dtype float or int\n        Relative or absolute numbers of training examples that will be used to\n        generate the learning curve. If the dtype is float, it is regarded as a\n        fraction of the maximum size of the training set (that is determined\n        by the selected validation method), i.e. it has to be within (0, 1].\n        Otherwise it is interpreted as absolute sizes of the training sets.\n        Note that for classification the number of samples usually have to\n        be big enough to contain at least one sample from each class.\n        (default: np.linspace(0.1, 1.0, 5))\n    """\n    plt.figure()\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel("Training examples")\n    plt.ylabel("Score")\n    train_sizes, train_scores, test_scores = learning_curve(\n        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n    train_scores_mean = np.mean(train_scores, axis=1)\n    train_scores_std = np.std(train_scores, axis=1)\n    test_scores_mean = np.mean(test_scores, axis=1)\n    test_scores_std = np.std(test_scores, axis=1)\n    plt.grid()\n\n    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n                     train_scores_mean + train_scores_std, alpha=0.1,\n                     color="r")\n    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n                     test_scores_mean + test_scores_std, alpha=0.1, color="g")\n    plt.plot(train_sizes, train_scores_mean, \'o-\', color="r",\n             label="Training score")\n    plt.plot(train_sizes, test_scores_mean, \'o-\', color="g",\n             label="Cross-validation score")\n\n    plt.legend(loc="best")\n    return plt\n\n# cv = ShuffleSplit(n_splits=3, test_size=0.2, random_state=0)\n# title = "Learning Curves (RFR, n_estimators=100)"\n# estimator = RandomForestRegressor(n_estimators=100)\n# plot_learning_curve(estimator, title, \n#                     df_train_data, df_train_target, ylim=(0.7, 1.01), cv=cv, n_jobs=4)\n# plt.show()\''}, {'role': 'assistant', 'content': 'result_visualization'}]