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

Last updated: November 22,2024

1. Install "weka-doc" package

This is a short guide on how to install weka-doc on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                weka-doc
            
                    
    
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2. Uninstall "weka-doc" package

Please follow the guidelines below to uninstall weka-doc on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                weka-doc
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the weka-doc package on Ubuntu 21.10 (Impish Indri)

Package: weka-doc
Architecture: all
Version: 3.6.14-2
Priority: optional
Section: universe/doc
Source: weka
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Java Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 45607
Recommends: weka
Filename: pool/universe/w/weka/weka-doc_3.6.14-2_all.deb
Size: 5493508
MD5sum: 1d0354d28800071da8401b87dd2be7fa
SHA1: 3f9e10b43c21ed9d66cf02cc1808c0a200694264
SHA256: 946c2432deb84450b8f029a50061a4986fc8efa1dc76415432dcf5cebb04f3e3
SHA512: 90ed397a39988d714c5a0ec28571bc6735478b94e09fd808fbdb9d36aca0ff16f625f9fc81b6c2f2e2b088e653dec10b64aadc1cef536016b836590b8cfe27db
Homepage: http://www.cs.waikato.ac.nz/~ml/weka/
Description-en: documentation for the Weka machine learning suite
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 documentation.
Description-md5: 08b28b3a5cf08cccf14461c871c136a1