I am looking at explaining a single prediction for a linear model:
Y = F(X) = x0 + a1x1 + a2x2 + ... anxn i.e. F: X -> Y
i.e. given a single instance z in X, return the relative contribution of each feature to the prediction of z. I could look at the coefficient multiplied by the value as a ratio the total value, but this does not indicate the relative impact of that feature (without scaling).
My question is: does it make sense to apply LIME analysis to a linear model for individual feature importances? Could the way that LIME analysis generates perturbations around a sample provide useful insight, despite the model being linear?