Introducing Salford Predictive Modeler 8

The Salford Predictive Modeler software suite includes the CART, MARS, TreeNet, Random Forests engines, as well as powerful new automation and modeling capabilities not found elsewhere.

The SPM software suite’s data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation.

The SPM software suite‘s automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. We package a complete set of results from alternative modeling strategies for easy review.


SPM’s CART modeling engine is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science.


The MARS modeling engine is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions.


Random Forests is a modeling engine that leverages the power of multiple alternative analyses, randomization strategies, and ensemble learning.


TreeNet Gradient Boosting is SPM’s most flexible and powerful data mining tool, capable of consistently generating extremely accurate models.


Features Including:


Tools to relieve gruntwork, allowing the analyst to focus on the creative aspects of model development.


Enhanced Algorithms

Regression, Classification, and Logistic Regression enhanced to support massive datasets.



New features for our core tools, based on user feedback and advances in data science.



Minitab helps companies and institutions to spot trends, solve problems and discover valuable insights in data by delivering a comprehensive and best-in-class suite of statistical analysis and process improvement tools. Combined with unparalleled ease-of-use, Minitab makes it simpler than ever to get deep insights from data. Unlock the value of your data with Minitab.


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Titus Corporation Is A Minitab Authorized Partner




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