On the value of popular crystallographic databases for machine learning prediction of space groups

•Crystallographic databases evaluated for space group prediction through composition-based classifiers and deep learning.•Greater generalizability seen in datasets with balanced space group distributions.•Composition-driven models capture decision rules that facilitate design of new materials for the most populated space groups.•New high entropy compounds belonging to the most populated space groups predicted with top-3 accuracy > 80%.