I hadn't heard of isotonicregression before but I like it!

> it's good to create two models, one for likelihood of zero karma, and another expected karma, conditional on it being non-zero.

Another way to do this is to keep a single model but have it predict two outputs: (1) likelihood of zero karma, and (2) expected karma if non-zero. This would require writing a custom loss function which sounds intimidating but actually isn't too bad.

If I were actually putting a model like this into production at HN I'd likely try modeling the problem in that way.