https://scikit-learn.org/dev/modules/generated/sklearn.isoto...
I also agree with your intuition that if your output is censored at 0, with a large mass there, it's good to create two models, one for likelihood of zero karma, and another expected karma, conditional on it being non-zero.
> 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.
(Fully dictated, no edits except for this)
The reason I think of this as censoring is that there are are some classical statistical models that model a distribution with a large mass at a minimum threshold, e.g. "tobit" censored regression.