Salford Predictive Modeler

Random Forests®


Breiman and Cutler’s Random Forests®

The Random Forests® modeling engine is a collection of many CART® trees that are not influenced by each other when constructed. The sum of the predictions made from decision trees determines the overall prediction of the forest. Random Forests’ strengths are spotting outliers and anomalies in data, displaying proximity clusters, predicting future outcomes, identifying important predictors, discovering data patterns, replacing missing values with imputations, and providing insightful graphics.


Cluster and Segment

Much of the insight provided by the Random Forests modeling engine is generated by methods applied after the trees are grown and include new technology for identifying clusters or segments in data as well as new methods for ranking the importance of variables. The method was developed by Leo Breiman and Adele Cutler of the University of California, Berkeley, and is licensed exclusively to Minitab.

Salford Predictive Modeler® 8

Minitab’s Integrated Suite of Machine Learning & Predictive Analytics Software



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 & data science.



MARS® (Multivariate Adaptive Regression Splines) methodology’s approach to regression modeling effectively uncovers important data patterns & 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®

Random Forests® helps to spot outliers & anomalies in data, display proximity clusters, predict future outcomes, identify important predictors, discover data patterns & provide insightful graphics.

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