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Well, I mean not actively maximizing your CV score, just accepting normally insufficient CV scores. For example, in a tree based regression with a very small n
by smartmic 4y ago
Well, I mean not actively maximizing your CV score, just accepting normally insufficient CV scores.
For example, in a tree based regression with a very small number of training samples, the leaves will almost resemble some of the training samples. A good generalization might not be possible at all. But this is not too bad if you know that your prediction samples have high similarity with at least one of the training samples. In the end, this is an edge case for Machine Learning but goes more into the direction of Expert Systems.
- isoprophlex 4y agoIs this a complicated way of saying "just use k-nearest neighbors"?
- MrMan 4y agoI think its implying that ML often works because the manifold assumption is partially correct. so samples will lurk in the same neighborhood.
- crabbygrabby 4y agoIn a lot of cases knn doesn't model systems appropriately unless you understand the space very well. At least knn offers some sort of theoretical assumption of the bayes error rate though so it's a decent tool.