# Difference between revisions of "MARS Algorithm"

m (Text replacement - "==References==" to "== References ==") |
|||

Line 10: | Line 10: | ||

** In [[statistics]], '''Multivariate adaptive regression splines (MARS)</B> is a form of [[regression analysis]] introduced by [[Jerome H. Friedman]] in 1991. It is a [[non-parametric regression technique]] and can be seen as an extension of [[linear model]]s that automatically models non-linearities and interactions between variables. The term "MARS" is trademarked and licensed to Salford Systems. In order to avoid trademark infringements, many open source implementations of MARS are called "Earth". <ref> [http://cran.r-project.org/web/packages/earth/index.html CRAN Package earth] </ref> <ref> [http://orange.biolab.si/blog/2011/12/20/earth-multivariate-adaptive-regression-splines/ Earth - Multivariate adaptive regression splines in Orange (Python machine learning library)] </ref> | ** In [[statistics]], '''Multivariate adaptive regression splines (MARS)</B> is a form of [[regression analysis]] introduced by [[Jerome H. Friedman]] in 1991. It is a [[non-parametric regression technique]] and can be seen as an extension of [[linear model]]s that automatically models non-linearities and interactions between variables. The term "MARS" is trademarked and licensed to Salford Systems. In order to avoid trademark infringements, many open source implementations of MARS are called "Earth". <ref> [http://cran.r-project.org/web/packages/earth/index.html CRAN Package earth] </ref> <ref> [http://orange.biolab.si/blog/2011/12/20/earth-multivariate-adaptive-regression-splines/ Earth - Multivariate adaptive regression splines in Orange (Python machine learning library)] </ref> | ||

<references/> | <references/> | ||

+ | |||

+ | === 2009 === | ||

+ | * ([[2009_TheElementsOfStatisticalLearning|Hastie et al., 2009]]) ⇒ [[::Trevor Hastie]], [[::Robert Tibshirani]], and [[::Jerome H. Friedman]]. ([[year::2009]]). “[https://web.stanford.edu/~hastie/Papers/ESLII.pdf The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition)]." Springer-Verlag. ISBN:0387848576 | ||

+ | ** QUOTE: ... [[2009_TheElementsOfStatisticalLearning|We]] describe five related techniques: [[generalized additive models]], [[trees]], [[multivariate adaptive regression splines]], the [[patient rule induction method]], and [[hierarchical mixtures of experts]]. ... | ||

---- | ---- | ||

[[Category:Concept]] | [[Category:Concept]] | ||

__NOTOC__ | __NOTOC__ |

## Revision as of 23:47, 13 September 2019

A MARS Algorithm is a non-parametric regression algorithm ...

**AKA:**Multivariate Adaptive Regression Splines.**See:**Decision Tree Training Algorithm, Regression Analysis, Non-Parametric Regression, Linear Model.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines Retrieved:2015-10-23.
- In statistics,
**Multivariate adaptive regression splines (MARS)**is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models non-linearities and interactions between variables. The term "MARS" is trademarked and licensed to Salford Systems. In order to avoid trademark infringements, many open source implementations of MARS are called "Earth".^{[1]}^{[2]}

- In statistics,

### 2009

- (Hastie et al., 2009) ⇒ [[::Trevor Hastie]], [[::Robert Tibshirani]], and [[::Jerome H. Friedman]]. (2009). “The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition)." Springer-Verlag. ISBN:0387848576
- QUOTE: ... We describe five related techniques: generalized additive models, trees, multivariate adaptive regression splines, the patient rule induction method, and hierarchical mixtures of experts. ...