  Supervised machine learning can be used to predict properties of string
geometries with previously unknown features. Using the complete intersection
Calabi-Yau (CICY) threefold dataset as a theoretical laboratory for this
investigation, we use low $h^{1,1}$ geometries for training and validate on
geometries with large $h^{1,1}$. Neural networks and Support Vector Machines
successfully predict trends in the number of K\"ahler parameters of CICY
threefolds. The numerical accuracy of machine learning improves upon seeding
the training set with a small number of samples at higher $h^{1,1}$.
