"Multivariate Adaptive Regression Splines (MARS) is an implementation of techniques popularized by Friedman (1991) for solving regression-type problems.

MARS is a nonparametric regression procedure that makes no assumption about the underlying functional relationship between the dependent and [explanatory] variables. Instead, MARS constructs this relation from a set of coefficients and basis functions that are entirely "driven" from the regression data. In a sense, the method is based on the "divide and conquer" strategy, which partitions the input space into regions, each with its own regression equation. This makes MARS particularly suitable for problems with higher input dimensions (i.e., with more than 2 variables), where the curse of dimensionality [see also blessing of dimensionality] would likely create problems for other techniques.

The MARSplines technique has become particularly popular in the area of data mining because it does not assume or impose any particular type or class of relationship (e.g., linear, logistic, etc.) between the predictor variables and the dependent (outcome) variable of interest. Instead, useful models (i.e., models that yield accurate predictions) can be derived even in situations where the relationship between the predictors and the dependent variables is non-monotone and difficult to approximate with parametric models." [Continue]

• Multivariate Adaptive Regression Splines, Jerome H. Friedman, The Annals of Statistics, Vol. 19, No. 1. (Mar., 1991), pp. 1-67.
• The Elements of Statistical Learning: Data Mining, Inference and Prediction, Hastie, T., Tibshirani, R. and Friedman, J.H. (2001), Springer Verlag, New York