Salford Launches Predictive Modeling Software Suite
Updated · Dec 16, 2010
Data mining and predictive analytics software provider, Salford Systems, has announced its new Salford Predictive Modeling (SPM) software suite. Noted in the company’s press release is that SPM provides businesses, institutions and government agencies with a highly accurate, ultra-fast platform for developing predictive, descriptive and analytical models from large and complex databases.
“SPM technology dramatically accelerates accurate, robust model generation by automatically sifting through such
databases to isolate significant patterns and relationships. Yet the program is easy to use for both technical and nontechnical
“In addition to powerful new proprietary automation and modeling capabilities, the Salford Predictive Modeling Suite
includes the company’s four data mining products:
“CART® (Classification and Regression Trees). Noted for its modeling accuracy, performance, visualization, feature set automation and ease of use, CART analyzes large and complex databases to generate classification and regression trees that quickly reveal important data patterns and relationships that could remain hidden using other analytical tools.
“TreeNet®. TreeNet is the proprietary technology underlying major recent advances in fraud detection, targeted marketing, and risk modeling. Its flexible, easy to use and learn technology enables users to create super-accurate, targeted marketing models and identify ultra-high lift segments with little analyst supervision. TreeNet has the advantage of being able to work effectively with dirty and erroneous data, separating problematic from reliable information. It is also responsible for the majority of the 14 data mining competitive wins Salford has secured in the last decade.
“MARS® (Multivariate Adaptive Regression Splines). Ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions. Its flexibility permits MARS to trace out non-linear patterns detected in the data. It can predict continuous numeric outcomes as well as probability models for yes/no outcomes.
“RandomForests®. Best suited for analyzing complex data structures embedded in small-to-moderate data sets for deep understanding. It uses the power of multiple alternative analyses, randomization strategies and ensemble learning to produce accurate models, insightful variable importance rankings and accurate reporting.”