After feature generation, SISSO performs linear regression to yield the model prediction, where each model is the scalar product of the SISSO-generated feature with a vector of fitted coefficients. Then, the models are ranked according to their prediction performance. Sure independence screening (SIS) is used to select optimal subspaces from the huge feature space.