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    Fast, highly accurate platform for data mining and predictive analytics

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    Fast, highly accurate platform for data mining and predictive analytics

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Random Forests® Feature Matrix

Feature Matrix and download PDF

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

ComponentsBasicProProExUltra
ComponentsBasicProProExUltra
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

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Tags: Frequently Asked Questions, FAQs, RandomForests, Support, Random Forests, SPM

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