How to Install and Uninstall weka-doc Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: December 31,2024

1. Install "weka-doc" package

Please follow the instructions below to install weka-doc on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                weka-doc
            
                    
    
    Copied

2. Uninstall "weka-doc" package

Learn how to uninstall weka-doc on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                weka-doc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the weka-doc package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: weka-doc
Priority: optional
Section: universe/doc
Installed-Size: 42008
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Java Maintainers
Architecture: all
Source: weka
Version: 3.6.13-1
Recommends: weka
Filename: pool/universe/w/weka/weka-doc_3.6.13-1_all.deb
Size: 4763266
MD5sum: 3972925652915ed8c857bae63a9bb3f5
SHA1: 89fcb64503135edbd6598c3f272087f47329afa8
SHA256: 5417a5edce4660f90b96705541eaa06317e51b0cf98734a789207d493ed6ffc2
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 documentation.
Description-md5: 9ab6f7813a78c4243cc6fc92beb9f51a
Homepage: http://www.cs.waikato.ac.nz/~ml/weka/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu