randomforest statistics

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randomforest specifications


Unique identifier OMICS_19995
Name randomforest
Software type Package/Module
Interface Command line interface
Restrictions to use None
Output data A measure of the importance of the predictor variables and a measure of the internal structure of the data.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 4.6-14
stats, RColorBrewer, MASS, R(≥3.2.2)
Maintained Yes




No version available



  • person_outline Andy Liaw
  • person_outline Matthew Wiener
  • person_outline Vladimir Svetnik

Additional information

http://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

Publication for randomforest

randomforest citations


Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods

Sci Rep
PMCID: 5953923
PMID: 29765080
DOI: 10.1038/s41598-018-25578-3
call_split See protocol

[…] 71 package with default radial kernel and default parametersNaïve Bayes without smoothing using naiveBayes method from e1071 packageDecision Tree without pruning using tree packageRandom Forest using RandomForest package and default parameters (500 trees)Logistic Regression calculated using glm functionLinear Discriminant Analysis using lda function from MASS package with default parametersSupport […]


Association between angiogenesis and cytotoxic signatures in the tumor microenvironment of gastric cancer

PMCID: 5953302
PMID: 29785121
DOI: 10.2147/OTT.S162729

[…] st classifier, including 42 parameters from the Hallmark gene set, was trained to separate the group of cases with high infiltration levels from cases with low infiltration levels using the R package randomForest with 10,000 trees. Eight signaling pathways of Hallmark that directly characterized the inflammation were excluded (TNFA_SIGNALING_VIA_NFKB, IL6_JAK_STAT3_SIGNALING, INTERFERON_ALPHA_RESP […]


A machine learning model with human cognitive biases capable of learning from small and biased datasets

Sci Rep
PMCID: 5943317
PMID: 29743630
DOI: 10.1038/s41598-018-25679-z

[…] that were observed less than twice.All experiments were implemented using R (https://www.r-project.org). We used the e1071 package for SVM, the nnet package for NN, the glmnet package for LR, and the randomForest package for RF. NB, LSNB and eLSNB models were implemented within the R statistical computing environment using custom scripts. […]


Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

PLoS Genet
PMCID: 5940178
PMID: 29738521
DOI: 10.1371/journal.pgen.1007333

[…] The R package “randomForest” [] was used to build random forest classifiers using a variety of parameters to assess which were best for accuracy. We used out-of-bag (OOB) error rate to measure the performance of the […]


Acute Zika Virus Infection in an Endemic Area Shows Modest Proinflammatory Systemic Immunoactivation and Cytokine Symptom Associations

Front Immunol
PMCID: 5943559
PMID: 29774022
DOI: 10.3389/fimmu.2018.00821
call_split See protocol

[…] are infected patients with and without specific symptoms. Random Forest analysis () was utilized to classify the importance of cytokines in predicting ZIKV infection and clinical symptoms (R package “randomForest”). The Wilcoxon matched-pair test was used to compare cytokine plasma levels in the same subjects in the acute and recovery phases (p-value < 0.05). We used GraphPad Prism 6.0 software (G […]


Characterization of the enhancer and promoter landscape of inflammatory bowel disease from human colon biopsies

Nat Commun
PMCID: 5916929
PMID: 29695774
DOI: 10.1038/s41467-018-03766-z

[…] and one list for CDa vs. UCa.Random Forest analysis. We utilized the inherent ability for Random Forests (RFs) to rank features by their importance for classification accuracy. To do this we used the randomForest function from the randomForest R package (https://CRAN.R-project.org/package=randomForest) on Combat-normalized TC expression data (as described above in the “Exploratory data analysis” s […]


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randomforest institution(s)
Biometrics Research, Merck Research Laboratories, Rahway, NJ, USA; Molecular Systems, Merck Research Laboratories, West Point, PN, USA; Molecular Systems, Merck Research Laboratories, Rahway, NJ, USA

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