Unlock: No-Free-Lunch Theorem

For binary classification with 0-1 loss, no learning algorithm can succeed on every distribution: for any algorithm and any sample size m smaller than half the domain, some realizable distribution forces error at least 1/8 with probability at least 1/7. Universal learners do not exist; prior knowledge enters through the choice of hypothesis class.