missForest: Nonparametric missing value imputation using ran

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Ada Coda
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missForest: Nonparametric missing value imputation using random forest

Post by Ada Coda » Mon May 04, 2015 3:12 am

missForest: Nonparametric missing value imputation using random forest

Abstract: missForest imputes missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation and can be run in parallel to save computation time. missForest has been used to, among other things, impute variable star colors in an All-Sky Automated Survey (ASAS) dataset of variable stars with no NOMAD match.

Credit: Stekhoven, Daniel J.

Site: https://github.com/stekhoven/missForest
https://doi.org/10.1093/bioinformatics/btr597

Bibcode: 2015ascl.soft05011S

Preferred citation method: Please see citation information here: https://cran.r-project.org/web/packages ... ation.html

ID: ascl:1505.011
Last edited by Ada Coda on Mon May 13, 2019 5:42 pm, edited 1 time in total.
Reason: Updated code entry.

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