Additional MARS Features
CART®, MARS®, TreeNet®, and Neural Networks
MARS auto-discovers important variables in data and presents it in equation-based results that resemble conventional logistic regression. It is a data mining tool geared for statistically trained professionals and the underlying methodology is created by Jerome Friedman of Stanford University.
- 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).
SPM® 8 Product Versions
- The best of the best. For the modeler who must have access to leading edge technology available and fastest run times including major advances in ensemble modeling, interaction detection and automation. ULTRA also provides advance access to new features as they become available in frequent upgrades.
- For the modeler who needs cutting-edge data mining technology, including extensive automation of workflows typical for experienced data analysts and dozens of extensions to the Salford data mining engines.
- A true predictive modeling workbench designed for the professional data miner. Variety of supporting conventional statistical modeling tools, programming language, reporting services, and a modest selection of workflow automation options.
- Literally the basics. Salford Systems award winning data mining engines without extensions or automation or surrounding statistical services, programming language, and sophisticated reporting. Designed for small budgets while still delivering our world famous engines
The SPM Salford Predictive Modeler® software suite is a highly accurate and ultra-fast platform for creating predictive, descriptive, and analytical models from databases of any size, complexity, or organization. The SPM® software suite has automation that accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. While the analyst is always in full control, we optionally anticipate the analyst's next best steps and package a complete set of results from alternative modeling strategies for easy review. Do in one day what normally requires a week or more using other systems.
The Salford Predictive Modeler® software suite includes:
- This definitive classification tree was developed by world-renowned statisticians, including Doctors Jerome Friedman and Leo Breiman. CART is one of the most well-known data mining algorithms and is designed for both non-technical and technical users.
- Ideal for users who prefer results in a form similar to traditional regression while capturing essential non–linearities and interactions.
- TreeNet is Salford's most flexible and powerful data mining tool capable of consistently generating extremely accurate models. It has been responsible for the majority of modeling competition awards and demonstrates remarkable performance. The regression classification algorithm typically generates thousands of small decision trees built in a sequential error correcting process to converge a model.
- Random Forests®:
- Random Forests's features include prediction, clusters and segment discoveries, anomaly tagging detection and multivariate class description. The method was developed by Leo Breiman and Adele Cutler, both of the University of California, Berkeley.
New Components & Features available in version 8.0!
- Generalized Path Seeker is Jerry Friedman's approach to regularized regression. This technology offers high-speed lasso for extreme data set configurations with upwards of 100,000 predictors and possibly very few rows. Such sets are commonplace in gene research and text mining. This is both supremely fast and efficient.
- RuleLearner is a powerful post–processing technique that selects the most influential subset of nodes, thus reducing model complexity. RuleLearner allows the modeler to take advantage of the increased accuracy of very complicated TreeNet and Random Forests models, while still yielding the simplicity of CART models.
MARS Price Quote
Review of MARS in the Jan 2000 issue of PCAI Magazine
Citations of MARS in the literature
Friedmans Article on MARS Methodology
Forecasting Recessions: Can We Do Better on MARS?
Martian Chronicles: Is MARS better than Neural Networks?
MARS Special Features
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 University Program