Figure 8.

End-to-end process of RAR evaluation. For each subject in the dataset, based on the whole MILC class prediction and model parameters, we estimated the feature importance vector $\mathbf{e}$ using some interpretability method $g_i$. Later on, we validated these estimates against random feature attributions $g^{\mathbf{R}}$ using the RAR method and an SVM model. Through the SVM model’s performance when separately trained with different feature sets, we show that whole MILC model-estimated features were highly predictive compared to a random selection of a similar amount of features. Empirically, we show that $\xi ({\mathit{X}}^M|g_i)>\xi ({\mathit{X}}^M|g^{\mathbf{R}}$), where $\xi$ is the performance evaluation function (e.g., area under the curve) and ${\mathit{X}}^M$ refers to the modified dataset constructed based on only retained feature values.