Let's suppose that I have a dataset with 5 numerical features of which each of them has some missing values and all of them have only non negative values.
Some suggested ways to deal with missing data are:
- Remove the rows which have even one missing value
- Impute the missing values
I do not prefer (1) because then you miss some valuable information from the rest of the features for these rows.
I do not prefer (2) because in general (it depends on the application) it introduces quite a lot of noise in the data.
What I am thinking is to: 3. Replace the missing values with a unique value (eg -1 or -999)
As I said, in my example the features have only non-negative numbers so values such as -1 or -999 will be only encountered by the algorithm for missing data.
What are your thoughts on (3)?
What are the advantages and disadvantages of this approach?