I recently saw a question on the scikit-learn mailing list that I had wondered about. This is the formula to minimize the residual sum of squares.
The formula for the minimization is:
$$ \min _w \| Xw - y \|_2^2 $$
I think the formula says that we retain the minimum set of coefficients ($w$) that we found from the smallest squared difference of predicted responses minus the observed values, which is $\|Xw -y\|^2$
What does the subscript 2 in the formula refer to?