How to Install and Uninstall libai-decisiontree-perl Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 18,2024

1. Install "libai-decisiontree-perl" package

Learn how to install libai-decisiontree-perl on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libai-decisiontree-perl
            
                    
    
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2. Uninstall "libai-decisiontree-perl" package

Learn how to uninstall libai-decisiontree-perl on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                libai-decisiontree-perl
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libai-decisiontree-perl package on Ubuntu 20.10 (Groovy Gorilla)

Package: libai-decisiontree-perl
Architecture: amd64
Version: 0.11-1build2
Priority: optional
Section: universe/perl
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Perl Group
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 75
Depends: perl (>= 5.30.0-7), perlapi-5.30.0, libc6 (>= 2.4), libgraphviz-perl
Filename: pool/universe/liba/libai-decisiontree-perl/libai-decisiontree-perl_0.11-1build2_amd64.deb
Size: 22816
MD5sum: c20bfa81e78cf4a084f685808f15f3dd
SHA1: 6bd61232ba1973dc36bf8fd0ba7c44cf17d436ae
SHA256: ed6fa612f8261e6ec9ff630b76050438c70fcc928b465b363e0cdf8f9644a5d5
SHA512: 1a20b636c1bcb12555d32b701b2f0f46fa04b006a25955c9f021a05698816493d7018b3f7b89ae46e51c0f150439b0729d20a06efc084ebd72e2025e99cdc6d8
Homepage: https://metacpan.org/release/AI-DecisionTree
Description-en: module to create decision trees from training data
The AI::DecisionTree module automatically creates so-called "decision trees"
to explain a set of training data. A decision tree is a kind of categorizer
that use a flowchart-like process for categorizing new instances. This
implementation uses the gain obtained at each node in order to figure out
what are the most useful information in order to take decisions.
Description-md5: be5b849e4f8c832250a9ddf56c0900ed