# What are the drawbacks of using least squares loss for regression?

It seems to be like the most popular loss function for regression, for everything from OLS (it's in the name!) to sophisticated regularized regressions.

Why is it so popular and what are the drawbacks?

• This must be a duplicate, isn't it? It would be very surprising if this question had not been asked before. Commented Oct 31, 2017 at 19:04
• The first sentence in the question is a bit strange. It seems like the part not a complete subsentence. Commented Oct 31, 2017 at 20:52
• Richard, in fact the 118th question on this website touches on this topic and the answer by Jen contains very useful links to work of Gorard which mostly answers this question stats.stackexchange.com/questions/118 possibly the OP had a more sophisticated discussion in mind, but that would be very broad, and some direction from the OP would be nice. For instance, I do not get whether the OP has a focus on regularization or about norms / errors. Commented Oct 31, 2017 at 21:09

Ideally the loss function should reflect the losses that are caused to you by the forecast errors. So, in this ideal setup there are no advantages or disadvantages of loss functions so long they represent your losses appropriately.

For instance, if any over or under prediction by $\Delta y$ units of items sold leads to $\$110\times (\Delta y)^2$losses then there are no disadvantages of the$L(\Delta y)=(\Delta y)^2$loss function. It's just the reality whether you like it or not. Using any other loss function would have been simply wrong, not advantageous or disadvantageous. Unfortunately, almost nobody even tries to construct the true loss function these days. There could be many reasons why we don't do it anymore, but the practice is such that we choose loss functions based on convenience. This leads to them having advantages and disadvantages over each other. So, for instance, if your error distribution is Cauchy, then the least squares loss function will lead you to nowhere, since they're linked to the expectations (moments) which do not exist for Cauchy. On the other hand, the least absolute values will produce a solution for Cauchy, since they're linked to the median which does exist for this distribution. In this regard the least squares are less robust than absolute values. On a related note, the least squares models are sensitive to outliers. • Ideally the loss function should reflect the losses that are caused to you by the forecast errors. Do you mean the loss function used for fitting the model? If so, you are incorrect. Counterexample: suppose the true distribution is$N(\mu,\sigma^2)$, the real-world loss is absolute loss, and the quantity of interest is a point forecast of a new observation. According to you, we should estimate$\mu$by the sample median which corresponds to absolute loss, and then take it as the point forecast. ... Commented Nov 2, 2017 at 18:15 • ...I say you get better forecast accuracy if you use the maximum likelihood estimator for$\mu$, which in this case is the sample mean and corresponds to square loss (thus a mismatch!), and take it as the point forecast. This is simply because the maximum likelihood estimator for$\mu\$ is more efficient than the sample median. In any case, your answer does not seem to address the OP's question... Commented Nov 2, 2017 at 18:16
• OK, so you are starting a new example, right? Because you would be wrong if talking about my example. Commented Nov 2, 2017 at 18:56
• this is not my example, because in my example the real-world loss is absolute. Now in your example, I think your explanation misses the difference between model estimation and forecasting from the model. In estimation you want a precise estimate and you use the loss function that gives you it. Once you have it, you tailor the forecast to the real-world loss function. Your example only illustrates that the point forecast needs to be tailored to the real-world loss function. Commented Nov 2, 2017 at 19:33
• @kjetilbhalvorsen, I agree. I have pondered upon estimation vs. use of the model a lot in the recent years (also posted a few related questions on here on CV) and am still trying to form a coherent view of that. Still learning, hopefully progressing in the right direction... Commented Oct 10, 2018 at 13:05

The second most popular choice to minimizing the squared distance (L2 loss) of predictions and targets is the absolute distance (L1 loss).

The first big difference is that L2 loss places much more weight on outliers, because the squared distance is proportionally much bigger.

The second big difference is the assumed distribution around the trend. L2 loss assumes the residuals are gaussian, and L1 loss assumes they are laplacian. See this discussion for more details.

• Neither of those loss functions makes distributional assumptions, nor do they imply any such assumptions unless you assume they are associated with likelihoods. These points therefore could benefit from clarification or further elaboration.
– whuber
Commented Oct 31, 2017 at 16:42
• Linear least squares regression problems -- even those with elaborate basis expansions and interaction terms -- can be solved efficiently in closed form (iterative solutions are unnecessary), and this is also the case for least squares solutions with quadratic penalties on the coefficients (such as ridge regression or the "wiggliness" penalty in MGCV). This is a huge computational/practical advantage.
– Josh
Commented Oct 31, 2017 at 17:43
• Also, the link in this answer is a discussion about ridge regression or L2 regularization. It's not immediately obvious what that has to do with the loss function. In principle, parameters could be fit such that they minimize L1 error while being subject to an L2 penalty. The reverse of this -- L2 loss, L1 penalty -- is the LASSO.
– Josh
Commented Oct 31, 2017 at 17:51
• Comments are not for extended discussion; this conversation has been moved to chat. Commented Nov 1, 2017 at 18:11