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.
Introducing Salford Predictive Modeler® 8
Minitab's Integrated Suite of Machine Learning Software
CART®
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.
MARS®
The MARS® modeling engine is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions.
TreeNet®
TreeNet® Gradient Boosting is SPM’s most flexible and powerful data mining tool, capable of consistently generating extremely accurate models.
Random Forests®
Random Forests® is a modeling engine that leverages the power of multiple alternative analyses, randomization strategies, and ensemble learning.