• randomForest

  • Referenced in 145 articles [sw10639]
  • package randomForest: Breiman and Cutler’s random forests for classification and regression. Classification and regression ... based on a forest of trees using random inputs...
  • GeneSrF

  • Referenced in 64 articles [sw08254]
  • GeneSrF: gene selection with random forests (v. 20070524). GeneSrF is a web tool for gene ... selection in classification problems that uses random forest. Two approaches for gene selection are used...
  • SPOT

  • Referenced in 76 articles [sw06347]
  • based models such as CART and random forest; Gaussian process models (Kriging), and combinations...
  • pSuc-Lys

  • Referenced in 31 articles [sw16643]
  • proteins with PseAAC and ensemble random forest approach. Being one type of post-translational modifications ... balancing out skewed training dataset by random sampling, and (3) constructing an ensemble predictor ... fusing a series of individual random forest classifiers. Rigorous cross-validations indicated that it remarkably...
  • missForest

  • Referenced in 26 articles [sw19483]
  • missForest: Nonparametric Missing Value Imputation using Random Forest. The function ’missForest’ in this package ... mixed-type data. It uses a random forest trained on the observed values...
  • AFP-Pred

  • Referenced in 24 articles [sw22441]
  • Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Some creatures ... this work, we report a random forest approach ”AFP-Pred” for the prediction of antifreeze...
  • randomSurvivalForest

  • Referenced in 23 articles [sw08133]
  • randomSurvivalForest: Random Survival Forests. Random survival forests for right-censored and competing risks survival data...
  • ranger

  • Referenced in 16 articles [sw14498]
  • ranger: A Fast Implementation of Random Forests for High Dimensional Data ... software is a fast implementation of random forests for high dimensional data. Ensembles of classification ... most memory efficient implementation of random forests to analyze data on the scale...
  • iPPI-Esml

  • Referenced in 26 articles [sw22413]
  • classifier formed by fusing seven individual random forest engines via a voting system...
  • iPTM-mLys

  • Referenced in 26 articles [sw23948]
  • fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations...
  • party

  • Referenced in 25 articles [sw07330]
  • provides an implementation of Breiman’s random forests. The function mob() implements an algorithm...
  • iDNA-Prot

  • Referenced in 16 articles [sw22410]
  • identification of DNA binding proteins using random forest with grey model. DNA-binding proteins play ... grey model” and by adopting the random forest operation engine, we proposed a new predictor...
  • iPhos-PseEvo

  • Referenced in 17 articles [sw23953]
  • fusing an array of individual random forest classifiers thru a voting system. Rigorous jackknife tests...
  • iHyd-PseCp

  • Referenced in 15 articles [sw23952]
  • acid composition (PseAAC) and introducing the ”Random Forest” algorithm to operate the calculation. Rigorous jackknife...
  • abcrf

  • Referenced in 9 articles [sw21308]
  • package abcrf: ABC random forests for Bayesian parameter inference. Approximate Bayesian computation (ABC) has grown ... level. The approach relies on the random forest methodology of Breiman (2001) applied ... advocate the derivation of a new random forest for each component of the parameter vector...
  • iRNA-2methyl

  • Referenced in 14 articles [sw24530]
  • composition), followed by fusing 12 basic random forest classifier into four ensemble predictors, with each...
  • varSelRF

  • Referenced in 8 articles [sw08253]
  • package varSelRF: Variable selection using random forests. Variable selection from random forests using both backwards...
  • randomForestSRC

  • Referenced in 6 articles [sw14394]
  • randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC). A unified treatment of Breiman ... random forests for survival, regression and classification problems based on Ishwaran and Kogalur’s random...
  • iDHS-EL

  • Referenced in 10 articles [sw22426]
  • formed by fusing three individual Random Forest (RF) classifiers into an ensemble predictor. The three...
  • PREvaIL

  • Referenced in 10 articles [sw25073]
  • residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight...