Why do we square the errors in OLS? Why not use true values?
| Jared Baumeister | 08/26/26 | | Karlstack | 08/26/26 | | Jared Baumeister | 08/26/26 | | coalition of rabbis | 08/26/26 | | Jared Baumeister | 08/26/26 | | Jared Baumeister | 08/26/26 | | Jared Baumeister | 08/26/26 | | Jared Baumeister | 08/26/26 |
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Date: August 26th, 2026 12:17 AM Author: Jared Baumeister
Squaring has a real cost. Because a big error is punished so hard, a single wrong data point can drag the whole line towards itself. One student recorded as 8 hours and 3 marks, by a typo, will bend the line more than the other seven points together.
The absolute version is far more resistant to that, and it has a name, mean absolute error. It is a good choice when your data has outliers you cannot remove. It is used less often mostly because of the corner, and because squared error has clean mathematics behind it.
https://teachyourselfcoding.com/courses/learn-machine-learning-from-first-principles-using-javascript/lesson/why-we-square-the-error/?page_tab=overview
(http://www.autoadmit.com/thread.php?thread_id=5897341&forum_id=2...id..#50093513) |
Date: August 26th, 2026 8:40 PM Author: Jared Baumeister
"What I still don't understand is why OLS regression is still the default solution to the problem of linear regression. It seems to me that in the majority of practical situations, the costs associated with errors are linear or approximately linear. If I order 2 extra parts, I incur twice the unnecessary cost as compared to if I order 1 extra part. Therefore, the optimal solution that OLS produces will not correspond to an optimal solution in reality."
https://stats.stackexchange.com/questions/470626/why-is-using-squared-error-the-standard-when-absolute-error-is-more-relevant-to
(http://www.autoadmit.com/thread.php?thread_id=5897341&forum_id=2...id..#50095388) |
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