Empirical Loss

GROWING This note is being developed. It is readible but changes are being applied.
cards/machine-learning/measurement/empirical-loss.md Current Note ID: The unique ID of this note.
#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 $f(x_i)$.

Planted: by ;

Dynamic Backlinks to cards/machine-learning/measurement/empirical-loss:
Measures of Generalizability
To measure the generalization, we define a generalization error, $$ \begin{align} \mathcal G = …
Population Loss
The loss calculated on all the whole population
cards/machine-learning/measurement/empirical-loss Links to:
Measures of Generalizability
To measure the generalization, we define a generalization error, $$ \begin{align} \mathcal G = …
Population Loss
The loss calculated on all the whole population

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