How to Install and Uninstall science-machine-learning Package on Debian 12 (Bookworm)

Last updated: December 23,2024

1. Install "science-machine-learning" package

This guide let you learn how to install science-machine-learning on Debian 12 (Bookworm)

$ sudo apt update $ sudo apt install science-machine-learning

2. Uninstall "science-machine-learning" package

Please follow the step by step instructions below to uninstall science-machine-learning on Debian 12 (Bookworm):

$ sudo apt remove science-machine-learning $ sudo apt autoclean && sudo apt autoremove

3. Information about the science-machine-learning package on Debian 12 (Bookworm)

Package: science-machine-learning
Source: debian-science
Version: 1.14.5
Installed-Size: 31
Maintainer: Debian Science Team
Architecture: all
Depends: science-config (= 1.14.5), science-tasks (= 1.14.5)
Recommends: autoclass, gprolog, libfann-dev, libga-dev, liblinear-dev, libocas-dev, libsvm-dev, libvigraimpex-dev, mcl, octave-ga, python3-fann2, python3-genetic, python3-mdp, python3-mlpy, python3-opencv, python3-sklearn, python3-statsmodels, python3-torch, python3-vigra, r-cran-amore, r-cran-bayesm, r-cran-class, r-cran-cluster, r-cran-gbm, r-cran-mass, r-cran-mcmcpack, r-cran-mnp, r-cran-msm, r-cran-tgp, weka
Suggests: flann, libcv-dev, libevocosm-dev, libmkldnn-dev, libroot-math-mlp-dev, libroot-montecarlo-vmc-dev, libroot-tmva-dev, libshark-dev, libshogun-dev, libtorch3-dev, lua-torch-graph, lua-torch-image, lua-torch-nn, lua-torch-nngraph, lua-torch-optim, lua-torch-trepl, lua-torch-xlua, pgapack, pybrain, python3-torch-sparse, root-system, science-numericalcomputation, science-statistics, science-typesetting, scilab-ann, torch-core-free, vowpal-wabbit, yap
Description: Debian Science Machine Learning packages
Description-md5: 7b159d266013cc188474cc1bc06f0ead
Homepage: https://wiki.debian.org/DebianScience/
Tag: role::metapackage, suite::debian
Section: science
Priority: optional
Filename: pool/main/d/debian-science/science-machine-learning_1.14.5_all.deb
Size: 10632
MD5sum: c8a1f3d996822bd2fb7f3d90a709b9fb
SHA256: 153779eef4ec46eab133a7209680d0c01acc11db22dacd528642259f5c3d3b52