Statistical learning with MARS

Steve Miller at the InformationManagement blog has been looking at predictive analytics tools for business intelligence applications, and naturally turns to the statistical modeling and prediction capabilities of R. Says Steve:

The R Project for Statistical Computing continues to dazzle in the open source world, with exciting new leadership at Revolution Computing promising to align commercial R with business needs.

The technique which is the subject of his most recent post is MARS: Multivariate Adaptive Regression Splines, available for R in the “earth” package:

“Mars is an adaptive procedure for regression, and is well suited for high-dimensional (i.e., a large number  of inputs). It can be viewed as a generalization of stepwise linear regression or a modification of the CART procedure to improve the latter’s performance in the regression setting.” 

Read on in Steve’s post for a more detailed review of MARS…

Steve Miller at the InformationManagement blog has been looking at predictive analytics tools for business intelligence applications, and naturally turns to the statistical modeling and prediction capabilities of R. Says Steve:

The R Project for Statistical Computing continues to dazzle in the open source world, with exciting new leadership at Revolution Computing promising to align commercial R with business needs.

The technique which is the subject of his most recent post is MARS: Multivariate Adaptive Regression Splines, available for R in the “earth” package:

“Mars is an adaptive procedure for regression, and is well suited for high-dimensional (i.e., a large number  of inputs). It can be viewed as a generalization of stepwise linear regression or a modification of the CART procedure to improve the latter’s performance in the regression setting.” 

Read on in Steve’s post for a more detailed review of MARS and its applications.

Information Management blogs: Predictive Models, Mars to Earth – Part 1

Link to original post