Theorems on worst case performance

No-free-lunch theorem

There is no single classifier that is general over the space of the possible problems

Ugly duckling theorem

Over all the possible pfratuer sets, there is no real way to measure similarity of points

Classifier evaluation

Break data into two chunks (training and test), hide the label for half the dataset.

Accuracy of a classifier is the number of correct predictions/test cases