- Automated Non-Linear Regression
- MARS software is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities 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. 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.
- High-Quality Probability
- 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.
- High-Performance Results
- Areas where MARS 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).