Machine Learning with intended missing values I have a dataset relating to humans completing reviews, the target variable is whether the review decision is correct / incorrect and one of my features is a trailing 4 week accuracy score for the reviewer.
These accuracy scores are not always available however. My question is around how to model this data - the fact that there is no available accuracy score might be a signal. From my research into this - everything I see tells me that the missing values must be imputed or removed. I am wondering whether there are techniques to incorporate the fact that the data is missing into the dataset.
Perhaps I could convert the score into a categorical variable {low, medium, high, not available] - would this be common practice? I am open to suggestions and would love to hear what is commonly done in these scenarios
The common case of missing values for which data is imputed or removed assumes that missing values appear randomly in the data, so the absence of value has no relevance to the task.
From your description, in your data the fact that a value is missing is significant by itself. So I'd say that yes, it makes sense in this case to represent this information as a categorical variable. Note that it can be represented as a special value for the score feature indeed, but it doesn't have to be the same variable.