[{'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:# for KNN  (tuning)\n\nn_neighbors=[]\nfor i in range (0,50,5):\n    if(i!=0):\n        n_neighbors.append(i)\nparams_dict={\'n_neighbors\':n_neighbors,\'n_jobs\':[-1]}\nclf_knn=GridSearchCV(estimator=KNeighborsRegressor(),param_grid=params_dict,scoring=\'neg_mean_squared_log_error\')\nclf_knn.fit(x_train,y_train)\npred=clf_knn.predict(x_test)\nmse = mean_squared_error(y_test, pred)\nr = r2_score(y_test, pred)\nmae = mean_absolute_error(y_test,pred)\nprint("Mean Squared Error:",mse)\nprint("R score:",r)\nprint("Mean Absolute Error:",mae)\''}, {'role': 'assistant', 'content': "['modelling']"}]