How to Install and Uninstall root-tmva.x86_64 Package on Fedora 36

Last updated: November 05,2024

1. Install "root-tmva.x86_64" package

This is a short guide on how to install root-tmva.x86_64 on Fedora 36


    
        
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                sudo dnf update
            
                    
    
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                sudo dnf install
            
                        
                root-tmva.x86_64
            
                    
    
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2. Uninstall "root-tmva.x86_64" package

This tutorial shows how to uninstall root-tmva.x86_64 on Fedora 36:


    
        
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                sudo dnf remove
            
                        
                root-tmva.x86_64
            
                    
    
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            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the root-tmva.x86_64 package on Fedora 36

Last metadata expiration check: 5:02:31 ago on Thu Sep 8 08:04:50 2022.
Available Packages
Name : root-tmva
Version : 6.26.06
Release : 1.fc36
Architecture : x86_64
Size : 2.0 M
Source : root-6.26.06-1.fc36.src.rpm
Repository : updates
Summary : Toolkit for multivariate data analysis
URL : https://root.cern/
License : BSD
Description : The Toolkit for Multivariate Analysis (TMVA) provides a
: ROOT-integrated environment for the parallel processing and
: evaluation of MVA techniques to discriminate signal from background
: samples. It presently includes (ranked by complexity):
:
: * Rectangular cut optimization
: * Correlated likelihood estimator (PDE approach)
: * Multi-dimensional likelihood estimator (PDE - range-search approach)
: * Fisher (and Mahalanobis) discriminant
: * H-Matrix (chi-squared) estimator
: * Artificial Neural Network (two different implementations)
: * Boosted Decision Trees
:
: The TMVA package includes an implementation for each of these
: discrimination techniques, their training and testing (performance
: evaluation). In addition all these methods can be tested in parallel,
: and hence their performance on a particular data set may easily be
: compared.