Additional MARS Features
This guide provides a brief introduction to MARS.
The following videos cover the underlying methods in the SPM® 8.2 Software Suite and provide demonstrations of each of the modeling engines.
Software Featured in the Videos:
- SPM® 8.2 Software Suite
- CART® Software
- RandomForests® Software
- TreeNet® Software
- MARS® Software
- RuleLearner™ Software
- ISLE© Software
- GeneralizedPathSeeker™ Software
MARS Special Features
Automatic Non-Linear Regression
The MARS® modeling engine is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions. The MARS methodology’s approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other regression methods to reveal. The MARS modeling engine builds its model by piecing together a series of straight lines with each allowed its own slope. This permits the MARS modeling engine to trace out any pattern detected in the data.
High-Quality Regression and Classification
The MARS Model is designed to predict 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. The MARS engine is also capable of producing high quality classification models for a yes/no outcome. The MARS engine performs variable selection, variable transformation, interaction detection, and self-testing, all automatically and at high speed.
Areas where the MARS engine has exhibited very high-performance results include forecasting electricity demand for power generating companies, relating customer satisfaction scores to the engineering specifications of products, and presence/absence modeling in geographical information systems (GIS).
MARS Supported File Types
The MARS® data-translation engine supports data conversions for more than 80 file formats, including popular statistical-analysis packages such as SAS® and SPSS®, databases such as Oracle and Informix, and spreadsheets such as Microsoft Excel and Lotus 1-2-3.
MARS System Requirements
MARS® (Multivariate Adaptive Regression Splines), introduced by Stanford University data mining guru Professor Jerome H. Friedman in 1988, is one of the landmarks in the evolution of regression methods. For the first time analysts could leverage a search mechanism intended to automatically discover nonlinearity and interactions in the context of classical regression.