How to Install and Uninstall root-tmva.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: November 29,2024

1. Install "root-tmva.x86_64" package

This is a short guide on how to install root-tmva.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)


    
        
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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

Please follow the step by step instructions below to uninstall root-tmva.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
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                sudo dnf remove
            
                        
                root-tmva.x86_64
            
                    
    
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                sudo dnf autoremove
            
                    
    
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3. Information about the root-tmva.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 1:22:34 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : root-tmva
Version : 6.30.04
Release : 1.el8
Architecture : x86_64
Size : 2.0 M
Source : root-6.30.04-1.el8.src.rpm
Repository : epel
Summary : Toolkit for multivariate data analysis
URL : https://root.cern/
License : BSD-3-Clause
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.