[{'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:medium_regression_tgt = (.5 + (9/13)) / 2\ndef run_cv1(train_df, cv_test, kf, features_to_use):\ntrain_X = train_df[features_to_use] #sparse.hstack([train_df[features_to_use], tr_sparse]).tocsr()\ntrain_y3 = np.array(train_df[\'interest_level\'].apply(lambda x: target_num_map[x]))\ntrain_y = np.zeros_like(train_y3, dtype=np.float32)\ntrain_y[train_y3 == 1] = medium_regression_tgt\ntrain_y[train_y3 == 2] = 1\ncv_preds = []\ncv_scores = []\nmodels = []\ntest_preds = []\nfold = 0\nfor dev_index, val_index in kf.split(range(train_X.shape[0]), train_y):\ndev_X, val_X = train_X.iloc[dev_index], train_X.iloc[val_index]\ndev_y, val_y = train_y[dev_index], train_y[val_index]\npreds, model = runXGB1(dev_X, dev_y, val_X, val_y)\nmodels.append(model)\ncv_scores.append(model.best_score)\nprint(cv_scores)\ncut_df = train_df.iloc[val_index]\nout_df = pd.DataFrame(preds)\nout_df.columns = ["level"]\nout_df["listing_id"] = cut_df.listing_id.values\nout_df[\'interest_tgt\'] = val_y # cut_df.interest.values\ncv_preds.append(out_df)\nxgtest = xgb.DMatrix(cv_test[fold][features_to_use])\ntest_preds.append(model.predict(xgtest, ntree_limit=model.best_ntree_limit))\ndf_cv = pd.concat(cv_preds)\nprint(np.sqrt(sklearn.metrics.mean_squared_error(df_cv.interest_tgt, df_cv.level)))\napreds = np.array(test_preds)\nreturn models, df_cv, apreds'}, {'role': 'assistant', 'content': 'modelling'}]