How to Install and Uninstall weka Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 17,2024

1. Install "weka" package

This is a short guide on how to install weka on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                weka
            
                    
    
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2. Uninstall "weka" package

This is a short guide on how to uninstall weka on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                weka
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the weka package on Ubuntu 20.10 (Groovy Gorilla)

Package: weka
Architecture: all
Version: 3.6.14-1
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Java Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 8047
Depends: default-jre | java7-runtime | java6-runtime, java-wrappers, cup (>= 0.11a+20060608)
Suggests: libsvm-java
Filename: pool/universe/w/weka/weka_3.6.14-1_all.deb
Size: 7246694
MD5sum: aa04c61e29293f93dd355197f5d788da
SHA1: 74670066a94d07ab8a5e88608bbc031baeac9bd6
SHA256: 1f6ccfce837b05a26c7a937e629ccad85a393c020b12eca91b8b8245f54670fa
SHA512: 3338a5f4a9ad42e4c94cc20b2ae3d6b84d6cfd47c395f54743ed4593f0c5ed7d295b36e372e367a6b9e8653c75354d05ceda85156a20ae6bd88b5e407f7e39cf
Homepage: http://www.cs.waikato.ac.nz/~ml/weka/
Description-en: Machine learning algorithms for data mining tasks
Weka is a collection of machine learning algorithms in Java that can
either be used from the command-line, or called from your own Java
code. Weka is also ideally suited for developing new machine learning
schemes.
.
Implemented schemes cover decision tree inducers, rule learners, model
tree generators, support vector machines, locally weighted regression,
instance-based learning, bagging, boosting, and stacking. Also included
are clustering methods, and an association rule learner. Apart from
actual learning schemes, Weka also contains a large variety of tools
that can be used for pre-processing datasets.
.
This package contains the binaries and examples.
Description-md5: f7b0cb5ee78d372ccf23e04524660343