Multivariate adaptive regression spline: Difference between revisions

Revision as of 22:03, 9 November 2021

Revision as of 08:50, 11 April 2022

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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|last=Kuhn|first=Max|last2=Johnson|first2=Kjell|date=2013|publisher=Springer New York|isbn=9781461468486|___location=New York, NY|language=en|doi=10.1007/978-1-4614-6849-3}}</ref> Compare the equation for ozone concentration above to, say, the innards of a trained [[Artificial neural network|neural network]] or a [[random forest]].

*MARS can handle both continuous and [[categorical data]].<ref>[[{{cite book | last=Friedman, J.| first=Jerome H.]] (1993)| ''chapter=Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines'' | authorlink=Friedman, J. H.|year=1993|title=New Directions in Statistical Data Analysis and Robustness (|editor=Morgenthaler, Ronchetti, Stahel,|publisher=Birkhauser}}</ref><ref edsname="Friedman 1991">{{cite web | last=Friedman | first=Jerome H.), Birkhauser| title=Estimating Functions of Mixed Ordinal and Categorical Variables Using Adaptive Splines | website=DTIC | date=1991-06-01 | url=https://apps.dtic.mil/sti/citations/ADA590939 | access-date=2022-04-11}}</ref> MARS tends to be better than recursive partitioning for numeric data because hinges are more appropriate for numeric variables than the piecewise constant segmentation used by recursive partitioning.

*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" />