Suppose you have a data set with some fields that you suspect are going to impact your study variable but whose value in many cases is null. What theory says is that we should remove empty row values, but if we do so for this case, we lose our prediction for about 50% of the cases plus the empty ones got also important information about a different category that you do not want to lose. What to do then?As example, think of an APP study in which you want to know the value of a categorical variable, say buy a product or not. Then you sent push notification to some users (not all) and some of them open the push and some of them don't. Hence some users will have the field push_open as a value from 0 to X and others as a null since you never sent them any push notification. Despite so, those guys to whom you did not send any push are many enough for us to do not want to discard them because of course from the set of users to whom you sent a push one expect different behaviour depending on whether they opened the push or not, and on the other hand the guys you did not sent any push are going to have probably a different behaviour to the two previous groups.Cheers!Sergi
Remove rows with empty values reduces accuracy
predictive modeling;data cleaning