Empirical Loss
#Data #Model Selection
Given a dataset with records $\{x_i, y_i\}$ and a model $\hat y_i = f(x_i)$ the empirical loss is calculated on all the records
$$ \begin{align} \mathcal L_{E} = \frac{1}{n} \sum_i^n d(y_i, f(x_i)), \end{align} $$
where $d(y_i, f(x_i))$ is the distance defined between $y_i$ and $f(x_i)$.
Published:
by L Ma;
L Ma (2021). 'Empirical Loss', Datumorphism, 02 April. Available at: https://datumorphism.leima.is/cards/machinelearning/measurement/empiricalloss/.
Current Ref:

cards/machinelearning/measurement/empiricalloss.md