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*MARS models are more flexible than [[linear regression]] models.
*MARS models are simple to understand and interpret.<ref name=":0">{{Cite book|title=Applied Predictive Modeling|
*MARS can handle both continuous and [[categorical data]].<ref>{{cite book | last=Friedman | first=Jerome H. | chapter=Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines | author-link=Friedman, J. H.|year=1993|title=New Directions in Statistical Data Analysis and Robustness |editor=Stephan Morgenthaler |editor2=Elvezio Ronchetti |editor3=Werner Stahel|publisher=Birkhauser}}</ref><ref name="Friedman 1991">{{cite
*Building MARS models often requires little or no data preparation.<ref name=":0" /> The hinge functions automatically partition the input data, so the effect of outliers is contained. In this respect MARS is similar to [[recursive partitioning]] which also partitions the data into disjoint regions, although using a different method. (Nevertheless, as with most statistical modeling techniques, known outliers should be considered for removal before training a MARS model.{{Citation needed|date=March 2019}})
*MARS (like recursive partitioning) does automatic [[Feature selection|variable selection]] (meaning it includes important variables in the model and excludes unimportant ones). However, there can be some arbitrariness in the selection, especially when there are correlated predictors, and this can affect interpretability<ref name=":0" />
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* [[Generalized additive model]]s. From the user's perspective GAMs are similar to MARS but (a) fit smooth [[Local regression|loess]] or polynomial [[Spline (mathematics)|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.
* [[TSMARS]]. Time Series Mars is the term used when MARS models are applied in a time series context. Typically in this set up the predictors are the lagged time series values resulting in autoregressive spline models. These models and extensions to include moving average spline models are described in "Univariate Time Series Modelling and Forecasting using TSMARS: A study of threshold time series autoregressive, seasonal and moving average models using TSMARS".
* [[Bayesian MARS]] (BMARS) uses the same model form, but builds the model using a Bayesian approach. It may arrive at different optimal MARS models because the model building approach is different. The result of BMARS is typically an ensemble of posterior samples of MARS models, which allows for probabilistic prediction.<ref>{{cite journal |last1=Denison |first1=D. G. T. |last2=Mallick |first2=B. K. |last3=Smith |first3=A. F. M. |title=Bayesian MARS |journal=Statistics and Computing |date=1 December 1998 |volume=8 |issue=4 |pages=337–346 |doi=10.1023/A:1008824606259 |s2cid=12570055 |url=https://link.springer.com/content/pdf/10.1023/A:1008824606259.pdf |language=en |issn=1573-1375}}</ref>
== See also ==
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