> Prediction accuracy can only be measured against records of something, and that record will be a distortion and simplification of reality.
Prediction accuracy can be measured against what actually happens. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.
You are still assuming everything that is recorded to be "like Bob" is the truth and captures reality clearly.
Moreover you need to give loans to everybody in order to check the accuracy of the algorithm. You can't just check a non random subset and expect to get non biased results.
> You are still assuming everything that is recorded to be "like Bob" is the truth and captures reality clearly.
Nope, just finding correlations between "records say Bob has bit 24 set" and "Bob paid his loans." The data could say that Bob is a pink space alien from Andromeda and the algorithm can still do something useful. Because if the data is completely random then it will determine that that field is independent from whether Bob will pay his loans and ignore it, but if it correlates with paying back loans then it has predictive power. The fact that you're really measuring something other than what you thought you were doesn't change that.
> Moreover you need to give loans to everybody in order to check the accuracy of the algorithm. You can't just check a non random subset and expect to get non biased results.
What you can do is give loans to a random subset of the people you wouldn't have to see what happens.
But even that isn't usually necessary, because in reality there isn't a huge cliff right at the point where you decide whether to give the loan or not, and different variables will place on opposite sides of the decision. There will be people you decide to give the loan to even though their income was on the low side because their repayment history was very good. If more of those people than expected repay their loans then you know that repayment history is a stronger predictor than expected and income is a weaker one, and if fewer then the opposite.
Prediction accuracy can be measured against what actually happens. If the algorithm says that 5% of people like Bob will default and you give loans to people like Bob and 7% of them default then the algorithm is off by 2%.