title: Synthetic learning machines.
abstract: BACKGROUND
  Using a collection of different terminal nodesize constructed random forests, each generating a synthetic feature, a synthetic random forest is defined as a kind of hyperforest, calculated using the new input synthetic features, along with the original features.
  
  
  RESULTS
  Using a large collection of regression and multiclass datasets we show that synthetic random forests outperforms both conventional random forests and the optimized forest from the regresssion portfolio.
  
  
  CONCLUSIONS
  Synthetic forests removes the need for tuning random forests with no additional effort on the part of the researcher. Importantly, the synthetic forest does this with evidently no loss in prediction compared to a well-optimized single random forest.
authors.affiliation: Division of Biostatistics, University of Miami, 1120 NW 14th Street, Miami, 33136 FL USA.
authors.forename: Hemant
authors.initials: H
authors.lastname: Ishwaran
authors.affiliation: Center for Information Technology, National Institutes of Health, Bethesda, 20892 MD USA.
authors.forename: James D
authors.initials: JD
authors.lastname: Malley
keywords: Machine; Nodesize; Random forest; Synthetic feature; Trees