[georgefox]

I'm probably nitpicking your language, but L1 regularization is precisely that: regularization. (See https://en.wikipedia.org/wiki/Regularization_(mathematics)#R....) In your typical linear regression setting, it does not replace the squared error loss but rather augments it. In regularized linear regression, for example, your loss function becomes a weighted sum of the usual squared error loss (aiming to minimize residuals/maximize model fit) and the norm of the vector of estimated coefficients (aiming to minimize model complexity).

[tomrod]

Hey, I appreciate your correction! I wrote my comment late and night and definitely mashed the details. Your nice "nitpick" is a much needed correction to my inaccuracy.