There is one topic that’s always my twitter Achilles heel: stats “vs” ML debates. Their existence frustrates me and I can never help myself. So, a few months back, I promised myself that the next time one came up, instead of entering the scrum, I’d just write a post. So.. 1/3https://twitter.com/JFutoma/status/1222837860803497984 …
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Interesting - I'd say MLers do view the data as a sample from a population, but don't necessarily care what this distribution is as long as they can make accurate predictions about samples drawn from it (i.e. test set performance- where test is, as you say, from same population)
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... that's "supervised MLers" to me more accurate I suppose. Also important to care about sampling from a population in online learning, where your population (generative distribution) may be changing over time?
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