Empirical Risk

machine-learning

Definition

Empirical Risk

Empirical Risk $R_{emp}(h)$, $h$ being a model, is the average loss calculated on a finite training dataset $S=\{(x_1,y_1),\dots ,(x_n,y_n)\}$. It serves as a practical, computable estimate of the true risk.

$$R_{emp}(h)=\frac{1}{n}\sum _{i=1}^{n}L(h(x_i),y_i)$$

Note that this is for a supervised setting.

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