very important features, but rarely input The features are of capital gain and capital loss, but very small amounts of people have one or the other. As far as I can see it may not signify any great difference, though I'm not sure how I would go about even normalising or whatnot these features:

     age        wgt       sex   gain        loss         hpw        
0   0.379310    0.119825    1   2174           0         -0.078664          
1   0.568966    0.131898    1   0              0         -2.327679          
2   0.362069    0.407596    1   0              0         -0.078664


The gain can skyrocket to 100,000 and that is definitely of significance considering I'm predicting whether someone will make  over $50k, though they occur at such a small rate, close to 3-5% that I'm not sure how I could go about implementing them. I doubt imputing data would be a good idea considering just about everyone is on 0 for both results.

Also to note is I have 15 columns total (many more with get_dummies()).
A tree-based model, especially one that allows missing data in the input, seems a good choice: the tree(s) can split large capital gains out (presumably separating a nearly pure node from the rest).

Also consider something more basic: just hard-code an exception for those with large gains, and model for the rest.