MARS® (Multivariate Adaptive Regression Splines), introduced by Stanford University data mining guru Professor Jerome H. Friedman in 1988, is one of the landmarks in the evolution of regression methods. For the first time analysts could leverage a search mechanism intended to automatically discover nonlinearity and interactions in the context of classical regression.
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.
MARS is not a black box. It is faster, more interpretable, and more accurate than neural nets.
The major advantage of MARS is that it automates aspects of regression modeling that are difficult and time-consuming. These include:
MARS is capable of predicting with much higher resolution and accuracy, typically producing unique scores for every record in a database. In this way, MARS expands on the capabilities of decision trees for regression.
A MARS predictive model can be implemented in two ways. First, new databases can be scored directly by identifying the MARS model and the data to be scored. MARS will perform all the required data transformations and calculations automatically and output the predicted scores. Second, the MARS predictive equation can be exported as ready-to-run C and SAS®-compatible code that can be deployed in the user's application framework.