How to Install and Uninstall r-cran-randomforest Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 22,2024

1. Install "r-cran-randomforest" package

Please follow the steps below to install r-cran-randomforest on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-randomforest
            
                    
    
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2. Uninstall "r-cran-randomforest" package

Please follow the guidelines below to uninstall r-cran-randomforest on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-randomforest
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-randomforest package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-randomforest
Architecture: amd64
Version: 4.6-14-2build1
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 292
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, libc6 (>= 2.29)
Suggests: r-cran-rcolorbrewer, r-cran-mass
Filename: pool/universe/r/r-cran-randomforest/r-cran-randomforest_4.6-14-2build1_amd64.deb
Size: 211980
MD5sum: 5ff67a2a30fc6ec67e1506180d936281
SHA1: 2d3d00d122fe36a90620bdc90b2ac6ffa5e0e63e
SHA256: ccb9b065fe55a7d1022b86d5606307d734143c634ec4880032e8cc2f0ee94439
SHA512: d400d4ba9f6137c64c006e36fd19e2dcaf5c43629ccfca0e472a5cfc5832f84f277525323ddcf7b9a6543cc65f57240cb8a89e34ce79e48dbad0417412332136
Homepage: https://cran.r-project.org/package=randomForest
Description-en: GNU R package implementing the random forest classificator
RandomForest implements Breiman’s random forest algorithm (based on Breiman
and Cutler’s original Fortran code) for classification and regression. It can
also be used in unsupervised mode for assessing proximities among data points.
.
The technique uses multiple decision trees and combines their individual
votes.
Description-md5: 9c426f8863a49b037052e87707ecc6b1