How to Install and Uninstall weka Package on Ubuntu 21.10 (Impish Indri)

Last updated: December 24,2024

1. Install "weka" package

This guide let you learn how to install weka on Ubuntu 21.10 (Impish Indri)


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

Please follow the instructions below to uninstall weka on Ubuntu 21.10 (Impish Indri):


    
        
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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 21.10 (Impish Indri)

Package: weka
Architecture: all
Version: 3.6.14-2
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Java Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 13119
Depends: cup (>= 0.11a+20060608), default-jre | java7-runtime | java6-runtime, java-wrappers
Suggests: libsvm-java
Filename: pool/universe/w/weka/weka_3.6.14-2_all.deb
Size: 6626932
MD5sum: 5962d973647f6629a499f480a61657a8
SHA1: a8e52aeb690c09a84bbad39b18c1dfb8a151559d
SHA256: a381ad43c4b4109f653785ea4ec9ff3603da5434d21602acac5e8f1453e336c5
SHA512: 52ba566b1c3e11190b71a84b7cec441f5adb3015313075505b9fc51e8972aeb501856d4c80de4e93b1a4060bb1bff1a90183221dfa7ab18b2361ca1daaf9ecb9
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.
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This package contains the binaries and examples.
Description-md5: f7b0cb5ee78d372ccf23e04524660343