Dear professor, scikit-learn is really good for me to do some work using machine learning method. Here, I have two questions: 1）To do 5 fold cross-validation, when I use StratifiedKFold，I could get stratified folds that each fold contains approximately the same percentage of samples of each target class as the complete set. And, when I use GroupKFold, it ensures that the same group is not represented in both testing and training sets. I want to know whether there is a method to combine these two methods together? 2) When I use GridSearchCV to do parameter search, I use scoring="accuracy" as scoring function to choose the best parameters. And I find that I can only get the accuracy score from the 5 fold cross-validation. What can I do if I want to get other scores such as sensitivity, specificity, MCC at the same time? It means that I want to use accuracy to choose the best parameters and I want to get the scores of many scoring parameters at the same time when I do 5 fold cross-validation. Thank you. 熊瑶 北京大学深圳研究生院 化学生物学与生物技术学院 XIONG Yao G301, School of Chemical Biology & Biotechnology Peking University Shenzhen Graduate School Shenzhen 518055, Guangdong, P.R. China E-mail: xiongyao@pku.edu.cn or xiongy20121126@foxmail.com