I agree with your comment in the sense that 80% is not, as it seems to be, a very large number. One has to understand that in statistics the baseline technique

I agree with your comment in the sense that 80% is not, as it seems to be, a very large number. One has to understand that in statistics the baseline technique is the "random". In the case of genders for USA, a random technique would probably have around 50% accuracy (http://kff.org/other/state-indicator/distribution-by-gender/ http://kff.org/other/state-indicator/distribution-by-gender/), so 80% is just 30% more than the baseline (there's still the other 2/5 to cover). I used to work with Text-Mining techniques and it was quite easy to come up with a statistical metric that could reach 70%-75% accuracy, for almost all problems that I've dealt with (mainly in the sub-area of concept extraction - information retrieval). I assume it is similar for this guy, so that is why he reaches this kind of values very fast. For instance, with only the character frequency and order he reports results of 75% which, if you look carefully, is just 20%-25% above the random baseline, not "pure" 75%.. Then he trains a classifier (Random Forest) and extracts the most relevant features that the classifier used, which is existence of an 'a' character in the last two positions of the name (the order of 'a' feature seems quite irrelevant to me). With these features he reaches the 80% ("random" + 30%) accuracy which is a good number but still far. I would say that these two features are to be expected, as, at least in my mother tongue (Portuguese), feminine names end with an 'a' (eg. Maria, Ana, Roberta [vs Roberto - masc], etc..). But all in all it can be considered a good exercise!