Mean squared error (MSE) is the arithmetic mean of the squared differences between predictions and actual values. It can serve as a regression loss, but its familiarity is not a reason to choose it by default. Ask whether penalizing large errors strongly matches the system’s goal. section 5.1
Two checks make the choice more concrete:
- Does the loss reflect the outcome that matters? Selecting a loss deserves its own rationale, even when performance metrics have already been chosen. section 5.1
- Are unusually large forecast errors plausible? Minimizing squared error leads to an unbiased point forecast, but MSE’s sensitivity to very high errors makes it unsuitable for intermittent-demand items and can also matter for other count data. section 5.3.1
A loss choice needs a reason
For a cryptocurrency exchange-volume predictor, a design document that lists evaluation metrics but skips the loss-function decision leaves an important question unanswered: what training objective best supports the forecast’s intended use? section 5.1
Compare MSE with alternatives such as mean absolute error rather than treating either as the automatic regression choice. A different loss can improve a model, but changing the loss alone will not solve every system problem. section 5.1
