[{'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 cv(df, test, feats, y_name, k=5):\n        score, preds, fis = [], [], []\n        chunk = df.shape[0] // k\n        \n        for i in range(k):\n            if i+1 < k:\n                valid = df.iloc[i*chunk: (i+1)*chunk]\n                train = df.iloc[:i*chunk].append(df.iloc[(i+1)*chunk:])\n            else:\n                valid: df.iloc[i*chunk:]\n                train: df.iloc[:i*chunk]\n                    \n            rf = RandomForestRegressor(random_state=42, n_jobs=-1, n_estimators=100)\n            rf.fit(train[feats],train[y_name])\n            \n            score.append(mean_squared_error(valid[y_name],rf.predict(valid[feats])) ** (1/2))\n            \n            preds.append(rf.predict(test[feats]))\n            \n            fis.append(rf.feature_importances_)\n            \n            print(i, 'OK')\n        return score, preds, fis"}, {'role': 'assistant', 'content': "['modelling']"}]