[{'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.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.naive_bayes import MultinomialNB\n\nfrom sklearn.model_selection import cross_val_score\n\n\nmodels = [\n    RandomForestClassifier(n_estimators=500, max_depth=4),\n    MultinomialNB(),\n    LogisticRegression(random_state=4),\n]\nCV = 5\ncv_df = pd.DataFrame(index=range(CV * len(models)))\nprint(cv_df)\nentries = []\nfor model in models:\n  model_name = model.__class__.__name__\n  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n  print(accuracies)\n  for fold_idx, accuracy in enumerate(accuracies):\n    entries.append((model_name, fold_idx, accuracy))\nprint(entries)\ncv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])\nprint(cv_df)"}, {'role': 'assistant', 'content': "['modelling']"}]