Using random forest when not all the observations have data I have this given database:



I would like to predict column "y" using the columns "index_1","index_2","index_3" using the random forest classifier.

as you can see, column "size: does not have values for each observation. 

My question is : Can I still use random forest classifier when I don't have data for all the observations, and if yes, is it ok?should I give value (e.g "noData") to the empty cells? will it harm the prediction? or maybe no need?
In theory decision trees (and random forests) are able to deal with missing values in the data. But whether a particular implementation of the algorithm allows this (and how to use it with this implementation) depends on the specific package.