[{'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:%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom sklearn.learning_curve import learning_curve\ndef plot_learning_curve(train_sizes, train_scores, test_scores):\ntrain_mean = np.mean(train_scores, axis=1)\ntrain_std = np.std(train_scores, axis=1)\ntest_mean = np.mean(test_scores, axis=1)\ntest_std = np.std(test_scores, axis=1)\nplt.plot(train_sizes, train_mean,\ncolor='blue', marker='o',\nmarkersize=5, label='training accuracy')\nplt.fill_between(train_sizes,\ntrain_mean + train_std,\ntrain_mean - train_std,\nalpha=0.15, color='blue')\nplt.plot(train_sizes, test_mean,\ncolor='green', linestyle='--',\nmarker='s', markersize=5,\nlabel='validation accuracy')\nplt.fill_between(train_sizes,\ntest_mean + test_std,\ntest_mean - test_std,\nalpha=0.15, color='green')\nplt.grid()\nplt.xlabel('Number of training samples')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.ylim([0.0, 1.1])"}, {'role': 'assistant', 'content': 'result_visualization'}]