this post was submitted on 21 Oct 2024
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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.
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!
It's the line of best fit, not the line of good fit
Line of “least bad” fit
Best Linear Unbiased Estimator
One Line to rule them all
One Line to find them
One Line to bring them all
and in the data bind them
Looks like a successfully trained learning model, to me. (Sarcasm)
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.
(⌐■_■)
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!
Fine! I'll use a second order polynomial to fit this instead. But that's the last order I'm willing to go to!
They'll also try to linear fit even the most obvious exponential curve.
One line best-fits all
Gaussian: "Squint."
R^2=0.03
Oh, it's trending up. That's progress!
Dat spread tho
I don't know why you get credit at all; I do all the work. - Excel
Probably minimal surface ellipse.
And if that doesn't work, there's always factor analysis.
How can you argue with a word like "best' anyway ;)