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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. The MARS procedure involves a forward stepwise model building stage followed by a backwards elimination of unneeded predictors to arrive at surprisingly high performance models, all automatically. At the heart of the MARS algorithm is the search for "knots" or breaks in the range of a predictor allowing a regression model containing that predictor to have different slopes in each region. Breaking predictors into regions permits nonlinearity, and when interactions are constructed from regions of predictors, remarkable discoveries are enabled.