[{'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 GradientBoostingRegressor\nn_estimators = [ 1, 2, 4, 8, 16, 32, 64, 100, 200, 500, 1000, 2000]\ntrain_results = []\ntest_results = []\nfor estimator in n_estimators:\n   model = GradientBoostingRegressor(n_estimators=estimator)\n   model.fit(x_train, y_train)\n   from sklearn.metrics import mean_squared_error, r2_score\n   model_score = model.score(x_train,y_train)\n# Have a look at R sq to give an idea of the fit ,\n# Explained variance score: 1 is perfect prediction\n   y_predicted_train=model.predict(x_train)\n   train_results.append(mean_squared_error(y_train, y_predicted_train))\n   print('R2 sq: ',model_score)"}, {'role': 'assistant', 'content': "['modelling']"}]