Using , we can define a relative information gain by a model

$$\rho_C ^2 = 1 - \frac{ \exp( - 2 F(\theta_1) ) }{ \exp( - 2 F(\theta_0) ) },$$

where $F(\theta_0)$ is the Fraser information assuming the features and target variables are all independent variables while $F(\theta_1)$ is the Fraser information of a model that predicts the target variable using the features.

$\rho_C^2$ is also the explained variation as it indicates the dispersion in the data that is explained using the features.

Planted: by ;

References: