[{'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.ensemble import RandomForestRegressor\n\nparams_rf = {\n    \'max_depth\': [10, 30, 35, 50, 65, 75, 100],\n    \'max_features\': [.3, .4, .5, .6],\n    \'min_samples_leaf\': [3, 4, 5],\n    \'min_samples_split\': [8, 10, 12],\n    \'n_estimators\': [30, 50, 100, 200]\n}\n\nrf = RandomForestRegressor()\nrf_regressor = GridSearchCV(rf, params_rf, scoring=\'neg_root_mean_squared_error\', cv = 10, n_jobs = -1)\nrf_regressor.fit(X_train, y_train)\nprint(f\'Optimal depth: {rf_regressor.best_params_["max_depth"]}\')\nprint(f\'Optimal max_features: {rf_regressor.best_params_["max_features"]}\')\nprint(f\'Optimal min_sample_leaf: {rf_regressor.best_params_["min_samples_leaf"]}\')\nprint(f\'Optimal min_samples_split: {rf_regressor.best_params_["min_samples_split"]}\')\nprint(f\'Optimal n_estimators: {rf_regressor.best_params_["n_estimators"]}\')\nprint(f\'Best score: {rf_regressor.best_score_}\')\''}, {'role': 'assistant', 'content': "['modelling']"}]