How to Install and Uninstall python3-niaclass.noarch Package on Fedora 38
Last updated: January 10,2025
1. Install "python3-niaclass.noarch" package
This is a short guide on how to install python3-niaclass.noarch on Fedora 38
$
sudo dnf update
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$
sudo dnf install
python3-niaclass.noarch
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2. Uninstall "python3-niaclass.noarch" package
Please follow the instructions below to uninstall python3-niaclass.noarch on Fedora 38:
$
sudo dnf remove
python3-niaclass.noarch
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$
sudo dnf autoremove
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3. Information about the python3-niaclass.noarch package on Fedora 38
Last metadata expiration check: 0:25:50 ago on Sat Mar 16 16:59:57 2024.
Available Packages
Name : python3-niaclass
Version : 0.1.4
Release : 1.fc38
Architecture : noarch
Size : 29 k
Source : python-niaclass-0.1.4-1.fc38.src.rpm
Repository : fedora
Summary : Python framework for building classifiers using nature-inspired algorithms
URL : https://github.com/lukapecnik/NiaClass
License : MIT
Description : NiaClass is a framework for solving classification tasks using nature-inspired
: algorithms. The framework is written fully in Python. Its goal is to find the
: best possible set of classification rules for the input data using the NiaPy
: framework, which is a popular Python collection of nature-inspired algorithms.
: The NiaClass classifier support numerical and categorical features.
Available Packages
Name : python3-niaclass
Version : 0.1.4
Release : 1.fc38
Architecture : noarch
Size : 29 k
Source : python-niaclass-0.1.4-1.fc38.src.rpm
Repository : fedora
Summary : Python framework for building classifiers using nature-inspired algorithms
URL : https://github.com/lukapecnik/NiaClass
License : MIT
Description : NiaClass is a framework for solving classification tasks using nature-inspired
: algorithms. The framework is written fully in Python. Its goal is to find the
: best possible set of classification rules for the input data using the NiaPy
: framework, which is a popular Python collection of nature-inspired algorithms.
: The NiaClass classifier support numerical and categorical features.