[{'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:b"# # \\n# from sklearn.ensemble import RandomForestRegressor \\n\\n# # \\n# rf = RandomForestRegressor(n_estimators=100,random_state=1,n_jobs=4) #  / /\\n\\n#; 10->    =>  100   /      / \\n#random_state =>  / n_jobs =4 , \\n\\n# # 100 ? =>       .  train set \\n#     . test set   a=b    \\n#      \\n\\n# #\\n# rf.fit(train2,np.log(train[\'count\']))\\n# #\\n# result = rf.predict(test2)\\n# test[\'count\']= result #test count   result   . \\n# test.head(10)\\n\\n# # column  ,       .    . \\n# #     \\n# #1.       .\\n# #2.     . \\n\\n\\n#   100 .    lgbm          . \\n# lgbm\\n# from lightgbm import LGBMRegressor\\n# lgb=LGBMRegressor()\\n# lgb.fit(train2,np.log(train[\'count\']))\\n# result=lgb.predict(test2)\\n# test[\'count\']=result\\n# test.head()\\n\\n#  lgbm xgboost   ? => X.   . \\n\\n#2 xgboost =>   .   100 .\\n# *  .    .   .\\n\\n# # ***\\n# tree =>\\n# tree .        => max_depth\\n#xgb=> max_depth 3 .       . \\n\\n\\nfrom xgboost import XGBRegressor\\nxgb=XGBRegressor(nthread=4,max_depth=5) #   .     . nthread=>   . n_jobs  . CPU  .\\nxgb.fit(train2,np.log(train[\'count\']))\\nresult=xgb.predict(test2)"'}, {'role': 'assistant', 'content': 'modelling'}]