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Are polynomial features the root of all evil? A myth (alexshtf.github.io)
1 point by hardmaru on Jan 23, 2024 | hide | past | favorite | 1 comment


First two sentences: "When fitting a non-linear model using linear regression, we typically generate new features using non-linear functions. We also know that any function, in theory, can be approximated by a sufficiently high degree polynomial."

Reaction after one glance at the graph right below the first paragraph: If the data points you are trying to fit look that sinusoidal, why the [bleep/] are you trying to fit them with polynomials, and not trig functions?

A couples sentences later: "But many blogs, papers, and even books tell us that high polynomials should be avoided. They tend to oscilate [sic] and overfit!"

Reaction: sounds to me like many of the authors in this field need to take a decent math and stats class or few - to learn about power series, and about the too-many-parameter problem, and about test-fitting curves to subsets of your data points to test the robustness of your analysis, and about the dangers of idiotic extrapolation from limited data.

Lazy meta-reaction: The article has been on HN for 40 minute now, and gotten zero upvotes. I'll hope that means that everyone here knows basic math & data analysis, and move on.




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