# Empirical Loss

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 $f(x_i)$.

Planted:
by L Ma;

Dynamic Backlinks to

`cards/machine-learning/measurement/empirical-loss`

:`cards/machine-learning/measurement/empirical-loss`

Links to:L Ma (2021). 'Empirical Loss', Datumorphism, 02 April. Available at: https://datumorphism.leima.is/cards/machine-learning/measurement/empirical-loss/.