This article mentions the words accuracy/accurate/accurately 10 times, e.g.
"when using his own dating-site-sourced dataset, was accurate at predicting the sexuality of males with 68 per cent accuracy – better than a coin flip"
But accuracy seems like a poor measure for something like this, when the population is highly unbalanced. It's trivial to create a classifier with high accuracy: just outputting 'heterosexual' every time would yield ~90% accuracy on faces of the general population.
This is true, which is why machine learning has long since learned to not even think of what you describe as a meaningful measure of accuracy. If you look at the linked paper [0], you'll find that the author uses the "ROC AUC" metric [1]:
>The ROC AUC score represents the probability that when given one randomly chosen positive instance and one randomly chosen negative instance, the classifier will correctly identify the positive instance
The article didn't mention AUC, so I assumed they were talking about accuracy in the sense people normally mean it, which also matches the definition in the sidebar of the wikipedia link you shared:
"when using his own dating-site-sourced dataset, was accurate at predicting the sexuality of males with 68 per cent accuracy – better than a coin flip"
But accuracy seems like a poor measure for something like this, when the population is highly unbalanced. It's trivial to create a classifier with high accuracy: just outputting 'heterosexual' every time would yield ~90% accuracy on faces of the general population.