Binary missing values

Presence of high quantities of missing data primarily within a column (as opposed to being distributed across rows and columns) can be a smell that the missing data might carry implicit meaning of a negative binary response.

Presence of high quantities of missing data primarily within a column—as opposed to being distributed across rows and columns—can be a smell that the data is not truly missing. The missing values in such cases may carry an implicit meaning of a negative binary response. This can be validated further by observing the column header along with the non-missing values of the feature(s) in question. If the non-missing data is indicative of a positive response such as ‘{t,T}rue’ or ‘{y,Y}es’ then the missing data may indicate a negative response.