Multivariate Adaptive Regression Splines was developed in the early 1990s by world-renowned Stanford physicist and statistician Jerome Friedman. It is an innovative, flexible modeling tool that automates the building of accurate predictive models for continuous and binary dependent variables. It excels at finding optimal variable transformations and interactions, the complex data structure that often hides in high-dimensional data. This approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other methods to reveal.