See Also
- Linear regression
- Segmented regression
- Generalized linear models (GLMs) can be incorporated into MARS models by applying a link function after the MARS model is built. Thus, for example, MARS models can incorporate logistic regression to predict probabilities.
- Non-linear regression is used when the underlying form of the function is known and regression is used only to estimate the parameters of that function. MARS, on the other hand, estimates the functions themselves, albeit with severe constraints on the nature of the functions. (These constraints are necessary because discovering a model from the data is an inverse problem that is not well-posed without constraints on the model.)
- Recursive partitioning (commonly called CART). MARS can be seen as a generalization of recursive partioning that allows the model to better handle numerical (i.e. non-categorical) data.
- Generalized additive models. From the user's perspective GAMs are similar to MARS but (a) fit smooth loess or polynomial splines instead of MARS basis functions, and (b) do not automatically model variable interactions. The fitting method used internally by GAMs is very different from that of MARS. For models that do not require automatic discovery of variable interactions GAMs often compete favorably with MARS.
- Rational function modeling
- Spline interpolation
Read more about this topic: Multivariate Adaptive Regression Splines
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