[{'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 i in range(200000):\ny_pred_rows = special.expit(np.dot(x_data_rows, w))  # shape = (x_row_num, 1)\ny_diff_rows = (y_data_rows - y_pred_rows)  # shape = (x_row_num, 1)\nw_grad = -np.dot(x_data_rows_trans, y_diff_rows)\nif total_epoch % 10000 == 0:\ntrain_error_count = np.sum(np.abs(to_bool(y_pred_rows) - y_data_rows))\ntrain_accuracy = float(x_row_num - train_error_count) / x_row_num\nmodel_file_info = "epo" + str(total_epoch) + "_acc" + str(train_accuracy)[:6]\nprint(model_file_info)\nvw = lr * w_grad + gamma * vw\nw = w - vw\ntotal_epoch += 1\ntrain_error_count = np.sum(np.abs(to_bool(y_pred_rows) - y_data_rows))\ntrain_accuracy = float(x_row_num - train_error_count) / x_row_num\nmodel_file_info = "epo" + str(total_epoch) + "_acc" + str(train_accuracy)[:6]\nprint(model_file_info)\nnp.savez(MODEL_FILE_PATH, w=w, vw=vw, lr=lr, total_epoch=total_epoch, x_train_means=x_train_means, x_train_stds=x_train_stds)'}, {'role': 'assistant', 'content': "['evaluation', 'modelling']"}]