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> "Accuracy remained high (69%) even when controlling for age, gender, and ethnicity." So if I just assign the majority label to all of the population of a giv
by cirenehc 6y ago
> "Accuracy remained high (69%) even when controlling for age, gender, and ethnicity."
So if I just assign the majority label to all of the population of a given demographics group, I would get the same result right? i.e., predicting "left" for all minorities under 30. You would also get ~70% accuracy.
- anigbrowl 6y agoWhat do you think 'controlling for age [,etc]' means?
- cirenehc 6y agoPlease state what you want to say. No need to be passive aggressive.
- anigbrowl 6y agoYou quoted the part about controlling for age, then described the sort of mistake that comes from not controlling for age. So I would like to know what you think it means, in order to meet you where you are. I'm not expressing aggression toward you.
- cirenehc 6y agoMy interpretation of control is fixing all other variables (the ones they mentioned) except for the one being measured (political orientation). If that's not what they did I'm happy to learn.
- anigbrowl 6y agoIn that case I don't understand your original comment, as it describes the sort of mistake that arises when you don't control for other factors, but you appear to accept that they did.
- cirenehc 6y agoMy original comment said: > So if I just assign the majority label to all of the population of a given demographics group, I would get the same result right? i.e., predicting "left" for all minorities under 30. You would also get ~70% accuracy. I meant that even if you control age, gender, ethnicity, a very trivial predictor (i.e., always predicting the majority label) could yield similar performance. What I meant to say was that their model may not perform as well as they made it sound.