Overfitting is one of the types of error that can crop up with this, but the error type that article refers to is the kind you get when you run a linear regression on a data set containing one point; there are infinitely many optimally-fit solutions that model the data.
Er, I’m not sure what you mean the distinction to be here. Overfitting is the superclass of that, not the subclass, as overfitting still describes this problem even when you can’t perfectly describe your data (but there are many ways to do it optimally).
My mistake, I thought you were referring to overfitting with the connotation of a deliberate choice, like the manager who thinks he should fit a 9th-degree polynomial to some essentially linear data because “the line gets closer”.
The models used for economic or climate data are usually based on theory, giving them a sensible number of degrees of freedom that may or may not match up with how much calibration data; I would not class this as overfitting in the common use of the term, as all the degrees of freedom do have legitimate reason to be there.
‘Overfitting’, yes? I think I may have learned about that from Nate Silver
Overfitting is one of the types of error that can crop up with this, but the error type that article refers to is the kind you get when you run a linear regression on a data set containing one point; there are infinitely many optimally-fit solutions that model the data.
Er, I’m not sure what you mean the distinction to be here. Overfitting is the superclass of that, not the subclass, as overfitting still describes this problem even when you can’t perfectly describe your data (but there are many ways to do it optimally).
My mistake, I thought you were referring to overfitting with the connotation of a deliberate choice, like the manager who thinks he should fit a 9th-degree polynomial to some essentially linear data because “the line gets closer”.
The models used for economic or climate data are usually based on theory, giving them a sensible number of degrees of freedom that may or may not match up with how much calibration data; I would not class this as overfitting in the common use of the term, as all the degrees of freedom do have legitimate reason to be there.