Products
Upcoming Tradeshows
- ASA Statistical Practice
February 16, 2012 - February 18, 2012Orlando, FL, Booth 12 - Predictive Analytics World
March 05, 2012 - March 09, 2012San Francisco, CA Booth 224 - Statistical Learning and Data Mining III
March 15, 2012 - March 16, 2012Palo Alto, CA, Booth TBA - INFORMS OR
April 15, 2012 - April 17, 2012Huntington Beach, CA Software Workshop April 15, 1-2:45pm Booth 6 - JSM
July 28, 2012 - August 02, 2012San Diego, CA, Booth TBA
Frequently Asked Questions for MARS
MARS® is ideal for users who prefer results in a form similar to traditional regression while capturing essential non–linearities and interactions. The MARS approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other regression methods to reveal.
Conventional regression models typically fit straight lines to data. MARS approaches model construction more flexibly, allowing for bends, thresholds, and other departures from straight-line methods. MARS builds its model by piecing together a series of straight lines with each allowed its own slope. This permits MARS to trace out any pattern detected in the data.
The MARS model is designed to predict continuous numeric outcomes such as the average monthly bill of a mobile phone customer or the amount that a shopper is expected to spend in a web site visit. MARS is also capable of producing high quality probability models for a yes/no outcome. MARS performs variable selection, variable transformation, interaction detection, and self–testing, all automatically and at high speed.
Multivariate Adaptive Regression Splines was developed in the early 1990s by world-renowned Stanford physicist and statistician Jerome Friedman. It is an innovative, flexible modeling tool that automates the building of accurate predictive models for continuous and binary dependent variables. It excels at finding optimal variable transformations and interactions, the complex data structure that often hides in high-dimensional data. This approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other methods to reveal.
The major advantage of MARS is that it automates aspects of regression modeling that are difficult and time-consuming. These include:
- selecting which predictor variables to use
- handling missing values
- transforming variables, accounting for non-linear relationships
- detecting interactions
- self-testing, ensuring that the model will perform well on future data
The results are more accurate and complete than handcrafted models.