[{'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.model_selection import KFold\nkf = KFold(n_splits=10)\noutcomes = []\n    \nfold = 0\nfor train_index, test_index in kf.split(train_numeric_X):\n    fold += 1\n    X_train, X_test = train_numeric_X.values[train_index], train_numeric_X.values[test_index]\n    y_train, y_test = train_numeric_Y[\'ConfirmedCases\'].values[train_index], train_numeric_Y[\'ConfirmedCases\'].values[test_index]\n    pipeline.fit(X_train, y_train)\n    predictions = RF_model.predict(X_test)\n    accuracy = accuracy_score(y_test, predictions)\n    outcomes.append(accuracy)\n    print("Fold {0} accuracy: {1}".format(fold, accuracy))     \nmean_outcome = np.mean(outcomes)\nprint("\\\n\\\nMean Accuracy: {0}".format(mean_outcome)) \''}, {'role': 'assistant', 'content': "['modelling']"}]