Multivariate Adaptive Regression Splines

Several previous tutorials (i.e. linear regression, logistic regression, regularized regression) discussed algorithms that are intrinsically linear. Many of these models can be adapted to nonlinear patterns in the data by manually adding model terms (i.e. squared terms, interaction effects); however, to do so you must know the specific nature of the nonlinearity a priori. Alternatively, there are numerous algorithms that are inherently nonlinear. When using these models, the exact form of the nonlinearity does not need to be known explicitly or specified prior to model training. Rather, these algorithms will search for, and discover, nonlinearities in the data that help maximize predictive accuracy. This latest tutorial discusses multivariate adaptive regression splines (MARS), an algorithm that essentially creates a piecewise linear model which provides an intuitive stepping block into nonlinearity after grasping the concept of linear regression and other intrinsically linear models.