Real-world problems are not a random sampling from all possible problems and there’s plenty of structure to exploit, so invoking NFL in this context seems odd to me.
A real world competition isn’t a random sample of anything. It’s a selection of some problems, with some data. The performance of any algorithm will depend on fit to those problems, with those data.
My takeaways from the NFL theorems—the problems in the real world are some structured subset of all possible problems, and the performance of any generalizer for a problem will depend on fit to that problem.
Aren’t all these forecasting competitions using real data from real-world problems, and so NFL is irrelevant?
NFL not relevant to the real world? Would you like to elaborate?
Real-world problems are not a random sampling from all possible problems and there’s plenty of structure to exploit, so invoking NFL in this context seems odd to me.
A real world competition isn’t a random sample of anything. It’s a selection of some problems, with some data. The performance of any algorithm will depend on fit to those problems, with those data.
My takeaways from the NFL theorems—the problems in the real world are some structured subset of all possible problems, and the performance of any generalizer for a problem will depend on fit to that problem.
That’s not chopped liver.