Don't cite the No Free Lunch Theorem
Tldr; You probably shouldn’t be citing the "No Free Lunch" Theorem by Wolpert . If you’ve cited it somewhere, you might have used it to support the wrong conclusion. What it actually (vaguely) says is “You can’t learn from data without making assumptions”. The paper on the “No Free Lunch Theorem” , actually called " The Lack of A Priori Distinctions Between Learning Algorithms " is one of these papers that are often cited and rarely read, and I hear many people in the ML community refer to it when supporting the claim that “one model can’t be the best at everything” or “one model won’t always be better than another model”. The point of this post is to convince you that this is not what the paper or theorem says (at least not the one usually cited by Wolpert), and you should not cite this theorem in this context; and also that common versions cited of the "No Free Lunch" Theorem are not actually true. Multiple Theorems, one Name The first problem is