Salford Systems Predictive Modeling Suite (SPM) is a highly accurate and ultra-fast platform for creating predictive, descriptive, and analytical models from databases of any size, complexity, or organization. Salford SPM automation 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.

Salford Systems Predictive Modeling Suite (SPM) includes:

• CART: The definitive classification tree developed by world–renowned statisticians including Drs. Jerome Friedman and Leo Breiman. CART is one of the most well–known data mining algorithms. CART is considered to be the algorithm most responsible for bringing data mining out of the university and into the business world.
• MARS: Ideal for users who prefer results in a form similar to traditional regression while capturing essential non–linearities and interactions.
• TreeNet: TreeNet is Salford's most flexible and powerful data mining tool, capable of consistently generating extremely accurate models. TreeNet has been responsible for the majority of Salford’s modeling competition awards. TreeNet demonstrates remarkable performance for both regression and classification. The algorithm typically generates thousands of small decision trees built in a sequential error–correcting process to converge to an accurate model.
• RandomForests: RF features include prediction, clusters and segment discoveries, anomaly tagging detection and multivariate class description. The method was developed by Leo Breiman and Adele Cutler of University of California, Berkeley.
• GPS: Generalized Path Seeker is Jerry Friedman's approach to regularized regression; this technology offers high speed LASSO–style regression for extreme data set configurations with upwards of 100,000 predictors and possibly very few rows. Such data sets are commonplace in gene research and text mining and the new technology is both supremely fast and efficient.
• Rulefit: Rulefit is a powerful post–processing technique which selects the most influential subset of nodes, thus reducing model complexity. Rulefit allows the modeler to take advantage of the increased accuracy of very complicated TreeNet and RandomForests models while still yielding the simplicity of CART models.
• Interaction Detection: Built around powerful TreeNet technology, interaction detection machinery allows an analyst to see the variables as well as groups of variables ranked by their interaction strength.