How to Install and Uninstall libai-decisiontree-perl Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: May 20,2024

1. Install "libai-decisiontree-perl" package

This guide let you learn how to install libai-decisiontree-perl on Ubuntu 21.04 (Hirsute Hippo)


    
        
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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

Please follow the guidance below to uninstall libai-decisiontree-perl on Ubuntu 21.04 (Hirsute Hippo):


    
        
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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 21.04 (Hirsute Hippo)

Package: libai-decisiontree-perl
Architecture: amd64
Version: 0.11-1build3
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 (>= 5.32.0-4), perlapi-5.32.0, libc6 (>= 2.4), libgraphviz-perl
Filename: pool/universe/liba/libai-decisiontree-perl/libai-decisiontree-perl_0.11-1build3_amd64.deb
Size: 22900
MD5sum: 4fe29bdfa3b4c60fd4ccae611a7cac01
SHA1: 2b7f8001385b094c772dfba86b94cfb3829f8b35
SHA256: 15984838e84cb8d1c90254a7238f111dc9424c630800452304b65a3826133c49
SHA512: 4a5014475ca679ac8d8c217421e66a75f805634285df3def675a1afe4cc7291362cb36fb4d9c0769af4998613f82130aba096fe23c95fe78d9c846acc64369fb
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