748
Ah yes, regression (lemmy.world)
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[-] Bougie_Birdie 158 points 10 months ago

It's the line of best fit, not the line of good fit

[-] macarthur_park@lemmy.world 38 points 10 months ago

Line of “least bad” fit

[-] merari42@lemmy.world 10 points 10 months ago

Best Linear Unbiased Estimator

[-] frezik@midwest.social 126 points 10 months ago
[-] Corr@lemm.ee 39 points 10 months ago

That was a joy. Thank you for sharing

[-] credo@lemmy.world 7 points 10 months ago

I had a hard time reading. Could not stop laughing enough to get past the fourth paragraph. Sides hurt, would not recommend.

[-] TheOakTree@lemm.ee 17 points 10 months ago

Germanium My Ass

[-] CodexArcanum@lemmy.world 14 points 10 months ago

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.

[-] frezik@midwest.social 11 points 10 months ago

From what I heard, the guy actually did change to a CS major shortly after writing this.

[-] ornery_chemist@mander.xyz 2 points 10 months ago

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

[-] negativenull@lemmy.world 48 points 10 months ago

One Line to rule them all
One Line to find them
One Line to bring them all
and in the data bind them

[-] Aurenkin@sh.itjust.works 26 points 10 months ago

One line best-fits all

[-] merari42@lemmy.world 26 points 10 months ago* (last edited 10 months ago)

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!

[-] reinei@lemmy.world 17 points 10 months ago

Fine! I'll use a second order polynomial to fit this instead. But that's the last order I'm willing to go to!

[-] prex@aussie.zone 14 points 10 months ago
[-] Engywuck@lemm.ee 10 points 10 months ago

Well, some students of mines actually put some figure like that in al lab report...

[-] Whelks_chance@lemmy.world 16 points 10 months ago

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...

[-] Engywuck@lemm.ee 3 points 10 months ago

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!

[-] rustydrd@sh.itjust.works 9 points 10 months ago* (last edited 10 months ago)

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.

(⌐■_■)

[-] mindbleach@sh.itjust.works 8 points 10 months ago

Gaussian: "Squint."

[-] Gork@lemm.ee 8 points 10 months ago

Dat spread tho

[-] Flocklesscrow@lemm.ee 6 points 10 months ago

I don't know why you get credit at all; I do all the work. - Excel

[-] MajorHavoc@programming.dev 5 points 10 months ago* (last edited 10 months ago)

Looks like a successfully trained learning model, to me. (Sarcasm)

[-] collapse_already@lemmy.ml 4 points 10 months ago

They'll also try to linear fit even the most obvious exponential curve.

[-] yesman@lemmy.world 4 points 10 months ago

And if that doesn't work, there's always factor analysis.

[-] troyunrau@lemmy.ca 4 points 10 months ago

How can you argue with a word like "best' anyway ;)

[-] uis@lemm.ee 2 points 10 months ago

Probably minimal surface ellipse.

this post was submitted on 21 Oct 2024
748 points (100.0% liked)

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