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That framing seems a wee bit dismissive of statistics, esp. applied stats -- I'll hazard a guess that this came from the ML camp. :)
by gmfawcett 5y ago
That framing seems a wee bit dismissive of statistics, esp. applied stats -- I'll hazard a guess that this came from the ML camp. :)
- srean 5y agoThere is nothing dismissive about it, they are just different things. If we have strong theoretical understanding of the physics/model of a problem, but are unsure about some parameters, it makes sense to develop methods to find those unknown parameters accurately. This ability is a big deal in traditional statistics. People working in this field try and prove results that their proposed method can actually do this. If the model happens to be largely correct and the method robust, this even allows prediction. Often, however, we do not know the model and the 'parameter' is a piece of fiction anyway. If we are interested in prediction alone, its fine to let go of an ability to accurately estimate the parameters as long as predictions are accurate. Think epicycle models of planetary motion. ML folks try to prove that their methods have good prediction properties and are happy to sacrifice on parameter recovery. Nonparametric statistics and prequential statistics are somewhere in the middle. Sometimes it does seem though that people are trying to out do each other, coming up with methods that estimates the spectrum of a unicorn's rainbow more accurately than the best known result in research literature. This may look odd because the unicorn and his rainbow are pieces of fiction.
- gmfawcett 5y agoFair points made, and your unicorn analogy is wonderful. :)