[{'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:import xgboost as xgb\n\nX = trainData.drop([\'count\', \'datetime\', \'registered\', \'casual\', \'date\', \'logcount\'], axis=1).values\nY = trainData[\'logcount\'].values\n\ntestX = testData.drop([\'datetime\', \'date\'], axis=1).values\n\ntrainMatrix = xgb.DMatrix(X, label=Y)\n\nmax_depth = 5\nmin_child_weight = 8\nsubsample = 0.9\nnum_estimators = 1000\nlearning_rate = 0.1\n\nclf = xgb.XGBRegressor(max_depth=max_depth,\n                min_child_weight=min_child_weight,\n                subsample=subsample,\n                n_estimators=num_estimators,\n                learning_rate=learning_rate)\n\nclf.fit(X,Y)\n\npred = clf.predict(testX)\npred = np.expm1(pred)\n\nsubmission = pd.DataFrame({\n        "datetime": timeColumn,\n        "count": pred\n    })\nsubmission.to_csv(\'XGBNoFE.csv\', index=False)\''}, {'role': 'assistant', 'content': "['save_results']"}]