3 ms·
When there is a small amount of information the variance of any estimation is very big and this explains what happens in that example. Overfitting implies a di
by sillymath3 3y ago
When there is a small amount of information the variance of any estimation is very big and this explains what happens in that example. Overfitting implies a different behavior in training and in test and this is related to a big variance in the estimation of the error. So small amount of information implies that any model suffer overfitting and big variance, so is a general result not related especifically with Bayes.