The SISSO approach starts with the collection of primary features. Then, a large number (e.g., up to millions) of expressions are generated by combining the primary features according to unary and binary mathematical operators such as addition, multiplication, division, and exponential. Finally, compressed sensing is applied to identify the few expressions, which combined by weighing coefficients, best correlate with the target for the dataset. The models identified by SISSO for an arbitrary target W have the general form