Breiman and Cutler’s Random Forests

Random Forests® software is a bagging tool that leverages the power of multiple alternative analyses, randomization strategies, and ensemble learning. Its strengths are spotting outliers and anomalies in data, displaying clusters, predicting future outcomes, identifying important predictors, replacing missing values with imputations, and providing insightful graphics.

Features

  • Automation: Varies the bootstrap sample size (Automate RFBOOTSTRAP)
  • Automation: Vary the number of randomly selected predictors at the node-level (Automate RFNPREDS)
  • RF modified version of random split point selection (RANDOMMODE, JITTERSPLITS options)
  • Random Split Point is now exposed in GUI
  • Breiman's 2000 theory paper measures of STRENGTH and CORRELATION in the forest. (CORR, BCORR)
  • Penalty configuration for RF engine
  • RF: preserve prototype nucleus and consider variations to prototype algorithm (SVPROTOTYPES, PROTOREPORT)
  • GUI RF Advanced tab
  • in-bag / out-of-bag indicator to diagnostics dataset to faciliate testing (SVDIAG)
  • Reporting of "raw" permutation-based variable importance
  • Accuracy-based variable importance to RF, classification first
  • Saving of "margins" to output dataset (SVMARGIN)

  • Alternative, non-bootstrap forms of tree-by-tree sampling ( SAMPLEAMOUNT, SAMPLEMODE, SAMPLEBYCLASS options)
  • New RF report: summarize N times each predictor appears in model, and N distinct split points
  • GUI controls for new Variable Importance measures
  • Flexible controls over interactions in a Random Forests for Regression model
  • Interaction strength reporting )
  • Spline-based approximations to the Random Forests for Regression dependency plots
  • Exporting Random Forests for Regression dependency plots into XML files
  • Build a CART tree utilizing the Random Forests for Regression engine to gain speed as well as alternative reporting
  • Automation: Explore the impact of influence trimming (outlier removal) for logistic and classification models (Automate INFLUENCE)
  • Automation: Exhaustive search and ranking for all interactions of the specified order (Automate ICL)

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