 The bias-variance trade-off therefore concerns the question of how complex a model ought to be to make reasonably accurate predictions on unseen or out-of-sample examples. The problem is to strike a balance between an under-fitting model, which erroneously ignores available information about the true function r, and an overfitting model, which erroneously includes information that is noise and thereby gives misleading information about the true function r.