We present the results of out-of-sample prediction by the physics-informed morphological features in the random forest (PIMF-RF) model and compare the accuracy with the CD-RF model that relies on volume fraction, total molar mass, temperature, and monomer identity of the diblock copolymers and the Curve-RF model that directly uses the original SAXS curve. We also employ bagging and the gradient boosting model with our feature set, which provides similar results detailed in the SI.