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Random Forests Special Features

Random Forests is included in the SPM® v8.2 suite, and general features apply. Additional features, special to Random Forests, are also included in varying versions of the suite.

Additional Random Forests Features are available in Pro, ProEx, and Ultra.

Modeling Engine: RandomForests for Classification o o o o
Additional Modeling Engine:RandomForests for Regession   o o o
Automation: Varies the bootstrap sample size (Automate RFBOOTSTRAP)   o o o
Automation: Vary the number of randomly selected predictors at the node-level (Automate RFNPREDS)   o o o
RF modified version of random split point selection (RANDOMMODE, JITTERSPLITS options)     o o
Random Split Point is now exposed in GUI     o o
Breiman's 2000 theory paper measures of STRENGTH and CORRELATION in the forest. (CORR, BCORR)     o o
Penalty configuration for RF engine     o o
RF: preserve prototype nucleus and consider variations to prototype algorithm (SVPROTOTYPES, PROTOREPORT)     o o
GUI RF Advanced tab     o o
in-bag / out-of-bag indicator to diagnostics dataset to faciliate testing (SVDIAG)     o o
Reporting of "raw" permutation-based variable importance      o o
Accuracy-based variable importance to RF, classification first      o o
Saving of "margins" to output dataset (SVMARGIN)     o o
Alternative, non-bootstrap forms of tree-by-tree sampling ( SAMPLEAMOUNT, SAMPLEMODE, SAMPLEBYCLASS options)     o o
New RF report: summarize N times each predictor appears in model, and N distinct split points      o o
GUI controls for new Variable Importance measures     o o
Flexible controls over interactions in a Random Forests for Regression model (requires TreeNet license)       o
Interaction strength reporting (requires TreeNet license)       o
Spline-based approximations to the Random Forests for Regression dependency plots (requires TreeNet license)       o
Exporting Random Forests for Regression dependency plots into XML files (requires TreeNet license)       o
Build a CART tree utilizing the Random Forests for Regression engine to gain speed as well as alternative reporting       o
Automation: Explore the impact of influence trimming (outlier removal) for logistic and classification models (Automate INFLUENCE)       o
Automation: Exhaustive search and ranking for all interactions of the specified order (Automate ICL)       o



Tags: RandomForests, Random Forests, Salford-Systems

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