[{'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:lgb_model = lgbm.LGBMRegressor(learning_rate=lgb_regressor.best_params_["learning_rate"], boosting=\'gbdt\', \n                               metric=\'l1\', feature_fraction=lgb_regressor.best_params_["feature_fraction"], \n                               num_leaves=20, min_data=10, max_depth=10, \n                               n_estimators=lgb_regressor.best_params_["n_estimators"], n_jobs=-1)\nlgb_model.fit(X_train, y_train)\ny_train_pred = lgb_model.predict(X_train)\ny_pred = lgb_model.predict(X_test)\nprint(\'Train r2 score: \', r2_score(y_train_pred, y_train))\nprint(\'Test r2 score: \', r2_score(y_test, y_pred))\ntrain_rmse = np.sqrt(mean_squared_error(y_train_pred, y_train))\ntest_rmse = np.sqrt(mean_squared_error(y_test, y_pred))\nprint(f\'Train RMSE: {train_rmse:.4f}\')\nprint(f\'Test RMSE: {test_rmse:.4f}\')\''}, {'role': 'assistant', 'content': "['modelling']"}]