ogrisel/scikit-learn — comment on commit b866ef92e2d5c785e0fa65856d670d34d97f40fe
File: doc/faq.rst line 121
By ogrisel:

The "how much better than chance" is indeed an important concept. 65% accuracy is a poor score for a binary classifier but is quite good if you are trying to find the class of an object on a picture among 100 or 1000 possible classes.

As for the false positive / false negative types of error, depending on the application both can be embarrassing. In NLP is usually better to favor precision over recall: it's often better to miss some semantic annotations in a knowledge extraction application and ensure that more than 95% of the extracted annotation are correct rather than pollute the user with noisy false positives. In this case too many false positives will cause rejection of the tool by the users.

Would you like to write FAQ sections on those topics and reference them from this question and from the howto_measure_classification_performance section?