scikit-learn 1.2.1-gfbf-2022b

Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.

Accessing scikit-learn 1.2.1-gfbf-2022b

To load the module for scikit-learn 1.2.1-gfbf-2022b please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2022b
module load scikit-learn/1.2.1-gfbf-2022b

BEAR Apps Version

2022b

Architectures

EL8-cascadelake — EL8-icelake — EL8-sapphirerapids

The listed architectures consist of two part: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

Extensions

• scikit-learn-1.2.1
• sklearn 0.0

More Information

For more information visit the scikit-learn website.

Dependencies

This version of scikit-learn has a direct dependency on: gfbf/2022b Python/3.10.8-GCCcore-12.2.0 SciPy-bundle/2023.02-gfbf-2022b

Required By

This version of scikit-learn is a direct dependent of: BEAR-Python-MSc-Bioinformatics/2022b-foss-2022b BioServices/1.11.2-foss-2022b mlxtend/0.22.0-gfbf-2022b MNE-Python/1.6.1-foss-2022b mofapy2/0.7.0-foss-2022b mvlearn/0.5.0-foss-2022b Nilearn/0.10.3-gfbf-2022b NLTK/3.8.1-foss-2022b pept/0.5.1-foss-2022b scikit-plot/0.3.7-foss-2022b

Other Versions

These versions of scikit-learn are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 26th July 2023