Highly imbalanced class Machine Learning I'm currently messing around on a wage dataset (trying to predict who did and didn't earn over 50k p.a. based on a range of factors). One of the variables - 'work-class' is very imbalanced and I was looking for some advice on how to deal with this.



100% of the people within the never-worked and without-pay categories earn over 50k.

Would imputation methods even be useful given the size of groups?

Thanks in advance,
Kelvin
What I'd do is to keep the Unknown class and treat the Never-worked and Without-pay as Unknown. 

Imputation methods would just reinforce the bias towards the most dominant class, i.e. private in your case. Say you used a simple classifier to impute these $21$ values, it would assign them to another class based on the similarity of the other features (e.g. age, education, ...). Think on what this procedure has done though, it has replaced someone with without-pay to private or something else. This is conceptually wrong, though, especially for a model that tries to predict the income of someone.