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Yeah, I agree that gaydar example illustrates your point, but it’s just such an egregious outlier that overall it just further refutes the idea that this distin
by mlthoughts2018 8y ago
Yeah, I agree that gaydar example illustrates your point, but it’s just such an egregious outlier that overall it just further refutes the idea that this distinction matters much.
About zero serious, mainstream papers or open-source libraries or business case studies each year resemble the poor experiment design from that gaydar paper. It’s a silly one to emphasize in terms of generalization error except I guess for basic 101 pedagogy for people who have never worked with models like this before.
- YeGoblynQueenne 8y agoIt's an especially egregious example, but everybody makes assumptions that they can't guarantee will hold.
- mlthoughts2018 8y agoThe first part of your comment doesn’t seem related to the second part, and neither part seems related to the question of whether most applied problems do or do not benefit from making a distinction between the sampling distribution used for generating training and test data and the “true” distribution.