Risk

Except the unavoidable error because of

Risk is decomposed into

Variance when loss is

Bias^2 + Variance + (Noise)

or generalized with

Loss Function (integral of loss function) In other words, the expected loss of a predictor over the distribution of data.

R(\hat{\phi}) = E[l(\hat{\phi}(X), Y)]

Risk Usages