scikit-learn 1.2.1-gfbf-2022bScikit-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, BEAR Cloud VMs, and CaStLeS VMs):
module load bear-apps/2022b
module load scikit-learn/1.2.1-gfbf-2022b
BEAR Apps Version
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR 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.
- BEAR Cloud and CaStLeS VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
- sklearn 0.0
For more information visit the scikit-learn website.
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 mofapy2/0.7.0-foss-2022b mvlearn/0.5.0-foss-2022b NLTK/3.8.1-foss-2022b pept/0.5.1-foss-2022b scikit-plot/0.3.7-foss-2022b
Last modified on 26th July 2023