ABSTRACT
This chapter discusses multivariate adaptive regression splines (MARS), an algorithm that automatically creates a piecewise linear model which provides an intuitive stepping block into nonlinearity after grasping the concept of multiple linear regression. MARS provide a convenient approach to capture the nonlinearity relationships in the data by assessing cutpoints similar to step functions. The MARS method and algorithm can be extended to handle classification problems and GLMs in general. MARS also requires minimal feature engineering and performs automated feature selection. The procedure assesses each data point for each predictor as a knot and creates a linear regression model with the candidate feature(s). The typical implementation of polynomial regression and step functions require the user to explicitly identify and incorporate which variables should have what specific degree of interaction or at what points of a variable X should cut points be made for the step functions.
