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.