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Woah that’s an unexpected and very hot take! What application of PCA are you familiar with that’s flawed? I learned it in the context of data science, where a)
by bbor 2y ago
Woah that’s an unexpected and very hot take! What application of PCA are you familiar with that’s flawed? I learned it in the context of data science, where a) parameter normalization is a very important setup step, and b) it has directly observable empirical success.
Like, I believe you that it’s used for Mathiness bs by some people, but disregarding it in its entirety seems like disregarding linear regression, or, idk, long division. I didn’t realize that was an option!
- mrbungie 2y agoI guess parent is talking about using PCA for doing something akin to Factor Analysis (and making interpretations over the components and the loadings / contribution from each variable towards each component). I have at least seen that in psychology, for example the Big 5 personality traits were extracted and then analyzed/discussed in that way, using FA though. I also don't think this is about disregarding the methods usefulness, but rather being very careful with assumptions and use critical thinking when interpretating results in studies that use a lot of math. In the case of DS/ML for prediction, it doesn't matter at all. As long as it empirically works, you're good. Unless you want very explainable/understandable models that is. PS: I don't like using wikipedia as a source, but you can take a look here as an example of what parent is talking about: https://en.m.wikipedia.org/wiki/Big_Five_personality_traits#Methodological_issues https://en.m.wikipedia.org/wiki/Big_Five_personality_traits#... . As I said, in this case is for FA, but a lot of people use PCA for making the same kind of studies, specially in less rigorous data-science for business settings.