I'm relatively new in the ML field, and this question came up when working with linear regression from sklearn library.
After a bit of looking up in the documentation, it states
Compute least-squares solution to equation Ax = b. Compute a vector x such that the 2-norm |b - A x| is minimized.
How does the least-squares solver find the |y - Ax| exactly? Maybe it's easy and I'm just overthinking it. May someone explain it with easy words, please? Just to know the overall mechanism behind it.
Thanks in advance
Edit: thanks a lot for the comments, I have now a better perspective of it.
