Hi All,
I am using a small data for binary classification problem with 320 rows. so as it is a small data set i am using all the data for training using cross validation.I have some missing values in the data and with out imputing any missnig values used gradient boosting algorithm and i got an accuracy of 83 % i would like to know what sort of missing values inputating is impeleted by gradient boosting and after that i used KNN algrrithmn to imput missing values and then applied gradint boosting and i got accuracy of 88 % .Could you please expain which one i should take as best?
Regards,
Vishnu