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

Last updated: May 22,2024

1. Install "weka-doc" package

Please follow the guidelines below to install weka-doc on Ubuntu 20.10 (Groovy Gorilla)


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                weka-doc
            
                    
    
    Copied

2. Uninstall "weka-doc" package

Learn how to uninstall weka-doc on Ubuntu 20.10 (Groovy Gorilla):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                weka-doc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the weka-doc package on Ubuntu 20.10 (Groovy Gorilla)

Package: weka-doc
Architecture: all
Version: 3.6.14-1
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: 43779
Recommends: weka
Filename: pool/universe/w/weka/weka-doc_3.6.14-1_all.deb
Size: 4773034
MD5sum: a80c6d391fcd9da3f5c470090e3bfb10
SHA1: 05ad641b6678e7c3013a36e6a0d270c660496875
SHA256: d2cd69b7451710481d83c5dbae746a4ecddaecf0f3b879ed7763dbe0885875ba
SHA512: d7c40228b42bc7e843c2d540be3921aff3a840d5421a8685c34a0c7b34463354038ab46b4b481f98e0544d60aa0b2789993911ec0ba583d5ee76238a52eedee4
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