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One Line to rule them all
One Line to find them
One Line to bring them all
and in the data bind themOne line best-fits all
Machine Learning enthusiasts: Why settle for linear regression when you can deploy a Gradient-Boosted Random Deep Neural Net Surface Vector Cluster that consumes the entire power of Iceland to trace a perfect ∞-dimensional hypersphere around those blue points? Overparameterization is the future!
Fine! I’ll use a second order polynomial to fit this instead. But that’s the last order I’m willing to go to!
R^2=0.03
Oh, it’s trending up. That’s progress!
Well, some students of mines actually put some figure like that in al lab report…
You asked for a line, they gave you a line, what more can be asked for?
We used to intentionally add wrong data to our datasets so we could circle it afterwards, declare it “outlying data” and gain the extra points for spotting and documenting it.
I’m not bitter about my formal education, honest…
We used to intentionally add wrong data to our datasets so we could circle it afterwards, declare it “outlying data” and gain the extra points for spotting and documenting it.
Nice trick!
If you’re having proportion of explained variance problems I feel bad for you son,
I got ninety nine problems but a fit ain’t one.(⌐■_■)
I don’t know why you get credit at all; I do all the work. - Excel
And if that doesn’t work, there’s always factor analysis.
Dat spread tho
They’ll also try to linear fit even the most obvious exponential curve.
Gaussian: “Squint.”
Probably minimal surface ellipse.
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