I have a huge dataset for a binary classification problem (about 1.5 million rows), and the feature space is quite large (145 dimension).
Some of these features are factors (YES, NO), but there is missing data. So my question is:
1 - Should i drop the missing data and lose information (the response matrix is quite sparse)
2 - Model these factors as 3 levels? ie, instead of (YES, NO) they become (YES, NO, X) where X is for missing data.
The problem with option 2 is that there is a lot of rows with missing information, so i would lose a lot of samples.
EDIT: Actually, the missing values happen when an specific condition happens, so they are informative as they are not 'missing' in the normal way. Does this reinforce the approach 2 above?
Thanks for any insight!