  Modern statistical learning techniques have often emphasized prediction
performance over interpretability, giving rise to "black box" models that may
be difficult to understand, and to generalize to other settings. We
conceptually divide a prediction model into interpretable and non-interpretable
portions, as a means to produce models that are highly interpretable with
little loss in performance. Implementation of the model is achieved by
considering separability of the interpretable and non-interpretable portions,
along with a doubly penalized procedure for model fitting. We specify
conditions under which convergence of model estimation can be achieved via
cyclic coordinate ascent, and the consistency of model estimation holds. We
apply the methods to datasets for microbiome host trait prediction and a
diabetes trait, and discuss practical tradeoff diagnostics to select models
with high interpretability.
