L2 loss

A loss function that calculates the square of the difference between actual label values and the values that a model predicts. Due to squaring, L2 loss amplifies the influence of outliers. That is, L2 loss reacts more strongly to bad predictions than L1 loss.¹

$$L_2 loss = \sum_{i=0}^{n} ( y_i - \hat{y}_i )^2$$

See also

• Regression models typically use L2 loss as the loss function.
• Mean squared error is the average L2 loss per example.

Footnotes

1. developers.google.com/machine-learning/glossary#l2-loss ↩