[{'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_validation_curve(param_range, 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(param_range, train_mean,\ncolor='blue', marker='o',\nmarkersize=5, label='training accuracy')\nplt.fill_between(param_range, train_mean + train_std,\ntrain_mean - train_std, alpha=0.15,\ncolor='blue')\nplt.plot(param_range, test_mean,\ncolor='green', linestyle='--',\nmarker='s', markersize=5,\nlabel='validation accuracy')\nplt.fill_between(param_range,\ntest_mean + test_std,\ntest_mean - test_std,\nalpha=0.15, color='green')\nplt.grid()\nplt.xscale('log')\nplt.legend(loc='lower right')\nplt.xlabel('Parameter C')\nplt.ylabel('Accuracy')\nplt.ylim([0.0, 1.1])"}, {'role': 'assistant', 'content': "['evaluation', 'modelling', 'result_visualization']"}]