> The SSN name data seems almost certainly flawed to a small degree...I.e. It's just hard to believe that there are dozens of boys named Jennifer (and yet, stra

> The SSN name data seems almost certainly flawed to a small degree...I.e. It's just hard to believe that there are dozens of boys named Jennifer (and yet, strangely, no boys named Sue!)...but we're talking about infinitesimal rounding errors. The vast, vast majority of names are 99% one way or the other...with a few exceptions such as Leslie...though you can mitigate that by using older years of the SSN database. I agree that the SSN data has flaws, but I only took names with at least 20 people. But the classification is probably iffy, as some names are classified as male and female. > So I have to strongly disagree with OP that 80% accuracy is something to be astounded by when it comes to gender classification... I originally hypothesized, I could reach 90% accuracy, but I could only get up to 82% max. As stated in the blog, 80% is the accuracy of a mammogram detecting cancer in a 40-45 year old woman which is pretty good for 3 features! > I wonder how much more using Soundex would add to the accuracy? Creating a trained name classifier would be a fun project in service of a tool that could gender classify how masculine or feminine a made-up name sounds like...which would be a slightly useful tool if you were a fantasy fiction writer, though I suppose if you were to be a successful writer, your ear would be trained well enough for he purpose to not delegate it to a computational tool. This would be very interesting to see!