Mean squared error

The mean squared error (MSE) is a metric for the error and quality of fit of a statistical learning model. In other words, it is the mean of the squares of the errors ($y_i-\hat{f}(x_i)$).

$$\text{MSE}=\frac{1}{n}\sum _{i=1}^n(y_i-\hat{f}(x_i){)}^2$$

where $\hat{f}(x_i)$ is the predicted value from the model and $y_i$ is the actual response from the training data. Low MSEs indicate a lower error.

MSEs are mostly used for regression problems.

See also

• Root-mean-square error