Dear Data Science Community:
Please, for the love of everything good, stop using gender as your examples of dichotomous variables.

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Sorry, but gender is still statistically cis male and female. It is such a minority that it's statistically unusable data.
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So you’re so bad at math that you don’t understand how to handle edge cases?
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No, I'm saying it's not as simple as "stop". Not all genders are necessary data points, depending on the study.
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My initial complaint was to stop using them in examples of frameworks.
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Here, one of the most fundamental ML techniques: https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Gender_classification … This example makes me want to *die*
End of conversation
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