L1 loss
A loss function that calculates the absolute value of the difference between actual label values and the values that a model predicts.
Plain English Explanation
A loss function that calculates the absolute value of the difference between actual label values and the values that a model predicts. For example, here's the calculation of L1 loss for a batch of five examples: Absolute value of delta | --- | 1 | 1 | 3 | 2 | 1 | | 8 = L1 loss | L1 loss is less sensitive to outliers than L2 loss. The Mean Absolute Error is the average L1 loss per example.
where: - $n$ is the number of examples. - $y$ is the actual value of the label. - $\hat{y}$ is the value that the model predicts for $y$. --- See Linear regression: Loss in Machine Learning Crash Course for more information.
How is it used?
Practitioners refer to l1 loss when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.