Salford Predictive Modeler (SPM)
Machine Learning and Predictive Analytics Software
SPM® 8 is a Minitab’s integrated suite of Machine Learning software. It includes various data mining techniques like classification, clustering, association and prediction. Some of the other methods are regression, survival analysis, missing value analysis, data binning and many more. SPM algorithms are considered to be essential in sophisticated data science circles. SPM® 8 is a highly accurate and ultra-fast platform for developing predictive, descriptive, and analytical models from databases and datasets of any size, complexity, or organization. It accelerates the process of model building by conducting substantial portions of the model exploration and refinement process.
The SPM® Salford Predictive Modeler® software suite includes CART®, MARS®, TreeNet®, Random Forests®, as well as powerful new automation and modeling capabilities not found elsewhere.
CART® (Classification And Regression Trees) is one of the most important tools in modern data mining. It provides the ultimate classification tree that has revolutionized the field of advanced analytics and inaugurated the current era of data science.
MARS® (Multivariate Adaptive Regression Splines) methodology’s approach to regression modeling effectively uncovers important data patterns and relationships that are difficult for other regression methods to reveal.
TreeNet® demonstrates remarkable performance for both regression and classification. It is the most flexible and powerful data mining tool, capable of consistently generating extremely accurate models.
Random Forests® helps to spot outliers and anomalies in data, display proximity clusters, predict future outcomes, identify important predictors, discover data patterns, replace missing values with imputations, and provide insightful graphics.
SPM® feature list
70+ pre-packaged automation scenarios inspired by the way leading model
New features for our core tools, based on user feedback and advances in data science.
Tools to relieve gruntwork, allowing the analyst to focus on the creative aspects of model development.
Regression, Classification, and Logistic Regression enhanced to support massive datasets.