Machine learning

Multivariate Adaptive Regression Splines (MARS)

Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data.

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Sources

  1. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. DOI: 10.1214/aos/1176347963

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Referenced by

ScholarGateMARS (Multivariate Adaptive Regression Splines (MARS)). Retrieved 2026-06-04 from https://scholargate.app/en/machine-learning/mars