Algorithm
Mean Squared Error
Description
MSE measures the average of the squares of the errors—that is, the average squared difference between the
estimated values and the actual value. It penalizes larger errors more severely than smaller ones.
$$ \text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $$
Use Cases
Regression Loss Function