[{'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:from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n\ndef get_metrics(y_test, y_predicted):  \n    # true positives / (true positives+false positives)\n    precision = precision_score(y_test, y_predicted, pos_label=None,\n                                    average='weighted')             \n    # true positives / (true positives + false negatives)\n    recall = recall_score(y_test, y_predicted, pos_label=None,\n                              average='weighted')\n    \n    # harmonic mean of precision and recall\n    f1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')\n    \n    # true positives + true negatives/ total\n    accuracy = accuracy_score(y_test, y_predicted)\n    return accuracy, precision, recall, f1"}, {'role': 'assistant', 'content': "['evaluation', 'prediction']"}]