[{'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:for f in ["X0", "X1", "X2", "X3", "X4", "X5", "X6", "X8"]:\nlbl = preprocessing.LabelEncoder()\nlbl.fit(list(train_df[f].values))\ntrain_df[f] = lbl.transform(list(train_df[f].values))\ntrain_y = train_df[\'y\'].values\ntrain_X = train_df.drop(["ID", "y", "eval_set"], axis=1)\ndef xgb_r2_score(preds, dtrain):\nlabels = dtrain.get_label()\nreturn \'r2\', r2_score(labels, preds)\nxgb_params = {\n\'eta\': 0.05,\n\'max_depth\': 6,\n\'subsample\': 0.7,\n\'colsample_bytree\': 0.7,\n\'objective\': \'reg:linear\',\n\'silent\': 1,\n\'tree_method\':\'hist\'\n}\ndtrain = xgb.DMatrix(train_X, train_y, feature_names=train_X.columns.values)\nmodel = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round=300, feval=xgb_r2_score, maximize=True)\nfig, ax = plt.subplots(figsize=(12,18))\nxgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\nplt.show()'}, {'role': 'assistant', 'content': "['modelling']"}]