[{'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 xgboost import XGBRegressor\nfrom sklearn.metrics import mean_absolute_error ,explained_variance_score, mean_squared_error\n#########################################################################3\nfrom sklearn.ensemble import RandomForestRegressor\n\nparameters = {'max_depth':  list(range(6, 30, 10)),\n              'max_leaf_nodes': list(range(50, 500, 100)),\n              'n_estimators': list(range(50, 1001, 150))}\n\nparameters1 = {'max_depth':  [6],\n              'max_leaf_nodes': [250],\n              'n_estimators': [100]}\nfrom sklearn.model_selection import GridSearchCV\n\ngsearch = GridSearchCV(estimator=RandomForestRegressor(),\n                       param_grid = parameters, \n                       scoring='neg_mean_squared_error',\n                       n_jobs=4,cv=5,verbose=7)\n\ngsearch.fit(X, y)"}, {'role': 'assistant', 'content': "['modelling']"}]