The sure-independence screening and sparsifying operator (SISSO) method [@Ouyang2017] is an algorithm belonging to the field of artificial intelligence and more specifically supervised machine learning.
As a symbolic-regression technique, SISSO is used to identify low-dimensional, analytic functions, the so called descriptors, that best predict the labels of a target data set.
SISSO is introduced for both regression and classification tasks.
In practice, SISSO first constructs a large and exhaustive feature space of billions of potential descriptors by taking in a set of user-provided *primary features*, and then iteratively applying a set of unary and binary operators, e.g., addition, multiplication, exponentiation, and squaring.
In practice, SISSO first constructs a large and exhaustive feature space of trillions of potential descriptors by taking in a set of user-provided *primary features*, and then iteratively applying a set of unary and binary operators, e.g., addition, multiplication, exponentiation, and squaring.
From this exhaustive pool of candidate descriptors, the best ones are identified via sure-independence screening, from which the best low-dimensional linear models are found via an $\ell_0$ regularization.
Because symbolic regression generates an interpretable equation, it has become an increasingly popular method across scientific disciplines [@Wang2019a], [@Neumann2020], [@Udrescu2020a].
