[{'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:from sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import ShuffleSplit\ndef plot_learning_curve(model,title, X, y,n_jobs = 1, ylim = None, cv = None,train_sizes = np.linspace(0.1, 1, 5)):\n    \n    # Figrue parameters\n    plt.figure(figsize=(10,8))\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\'Training Examples\')\n    plt.ylabel(\'Score\')\n    \n    train_sizes, train_score, test_score = learning_curve(model, X, y, cv = cv, n_jobs=n_jobs, train_sizes=train_sizes)\n    \n    # Calculate mean and std\n    train_score_mean = np.mean(train_score, axis=1)\n    train_score_std = np.std(train_score, axis=1)\n    test_score_mean = np.mean(test_score, axis=1)\n    test_score_std = np.std(test_score, axis=1)\n    \n    plt.grid()\n    plt.fill_between(train_sizes, train_score_mean - train_score_std, train_score_mean + train_score_std,\\\\\n                    alpha = 0.1, color = \'r\')\n    plt.fill_between(train_sizes, test_score_mean - test_score_std, test_score_mean + test_score_std,\\\\\n                    alpha = 0.1, color = \'g\')\n    \n    plt.plot(train_sizes, train_score_mean, \'o-\', color="r", label="Training score")\n    plt.plot(train_sizes, test_score_mean, \'o-\', color="g", label="Cross-validation score")\n    \n    plt.legend(loc = "best")\n    return plt\''}, {'role': 'assistant', 'content': 'result_visualization'}]