  Discovery of new natural laws has for a long time relied on the inspiration
of some genius. Recently, however, machine learning technologies, which analyze
big data without human prejudice and bias, are expected to find novel natural
laws. Here we demonstrate that our proposed machine learning,
recursive-LASSO-based symbolic (RLS) regression, enables data-driven
formulation of natural laws from noisy data. The RLS regression recurrently
repeats feature generation and feature selection, eventually constructing a
data-driven model with highly nonlinear features. This data-driven formulation
method is quite general and thus can discover new laws in various scientific
fields.
