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).
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Our University Program provides the SPM®, CART®, MARS®, TreeNet® , and Random Forests® modeling engines at significantly-reduced licensing fees to the educational community.
70+ pre-packaged scenarios, basically experiments, inspired by how leading model analysts structure their work.