I'm playing around with UCI Bank Marketing Dataset. So, there is a categorical variable named
default which tells us if client "has credit in default". That variable has three options:
unknown. Look at the distribution of it:
no 32588 unknown 8597 yes 3
As you can see, we meet
yes in only 3 cases and my question is how to deal with such tiny categories in general? Should I just exclude that from the dataset every time I come across it? Or maybe I should make something like oversampling but merely for that cases?
I'm asking because I'm concerned about its impact on a classification task. As far as I understand, if all of these
yes will fall into
test parts of the dataset during partitioning, it will distort a metric's result.