It's the line of best fit, not the line of good fit
this post was submitted on 21 Oct 2024
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Line of “least bad” fit
Best Linear Unbiased Estimator
That was a joy. Thank you for sharing
Check this shit out (fig 1).
Lmao there's so much gold. I got frustrated for him while reading it.
That line, and then just by instinct going to Fig 1, and seeing its caption...incredible lol
I had a hard time reading. Could not stop laughing enough to get past the fourth paragraph. Sides hurt, would not recommend.
Germanium My Ass
Going into physics was the biggest mistake of my life. I should've declared CS. I still wouldn't have any women, but at least I'd be rolling in cash.
Honestly, there wasn't all that much cash to roll in and there's less all the time now. Plus, if you think busted equipment is bad, wait until I tell you about inheriting legacy code.
From what I heard, the guy actually did change to a CS major shortly after writing this.
This relation between temperature and resistivity can be shown to be exponential in certain temperature regimes by waving your hands and chanting "to first order."
for some reason this is the line that got me
One Line to rule them all
One Line to find them
One Line to bring them all
and in the data bind them
One 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!
Oh, it's trending up. That's progress!
R^2=0.03
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.
(⌐■_■)
Gaussian: "Squint."
Dat spread tho
I don't know why you get credit at all; I do all the work. - Excel
Looks like a successfully trained learning model, to me. (Sarcasm)
How can you argue with a word like "best' anyway ;)
They'll also try to linear fit even the most obvious exponential curve.
And if that doesn't work, there's always factor analysis.
Probably minimal surface ellipse.