Breiman and Cutler's Random Forests

 
 

Breiman and Cutler's Random Forests

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Random Forests®

Random Forests

Breiman and Cutler’s Random Forests®:

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.

Suited for Wide Datasets:

Random Forests 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 is best suited for the analysis of complex data structures embedded in small to moderate data sets containing less than 10,000 rows but potentially millions of columns.

 

Tags: Salford Predictive Modeler, Random Forests, Salford-Systems

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