> machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved The N

> machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved The NFL theorems essentially state that for every dataset on which an algorithm generalizes well, there is another on which it generalizes poorly. So, there are always implicit biases, even for a random forest model (eg: if you try to model Boolean functions with a random forest, those functions which can be approximated effectively with few trees will form a set of very small measure. Specifically, it is my intuition that those which have many terms in the sum of products form might need many trees to approximate). It then becomes a question of whether the bias of your models are compatible with the dataset/domain under consideration. See this Minsky-Sussman koan: http://www.catb.org/jargon/html/koans.html#id3141241 http://www.catb.org/jargon/html/koans.html#id3141241 > Both statistical and ML models suffer from the problem you're stating. And that is precisely why I don’t see how ML algorithms are “more powerful” than statistics in any way, as per your claim.