  The no-free-lunch theorems promote a skeptical conclusion that all possible
machine learning algorithms equally lack justification. But how could this
leave room for a learning theory, that shows that some algorithms are better
than others? Drawing parallels to the philosophy of induction, we point out
that the no-free-lunch results presuppose a conception of learning algorithms
as purely data-driven. On this conception, every algorithm must have an
inherent inductive bias, that wants justification. We argue that many standard
learning algorithms should rather be understood as model-dependent: in each
application they also require for input a model, representing a bias. Generic
algorithms themselves, they can be given a model-relative justification.
