> But in a binary classification setting, that's not how the data would be presented to the model. Instead you would have one row per customer with a churn/no churn label for that customer along with values for a number of independent variables you deem relevant. The reason I put it in that 2x2 table like that is just to make the problem more apparent.

I see. Thanks for clarifying that.

> If I understand you correctly, what you're talking about is modifying the likelihood to be flatter so that it can't get caught in this one localised whirlpool as easily.

Effectively, yes. But by adding a term for the uncertainty on the measurements, not an uncertainty on the fit parameters (though those exist as well).