scikit-learn 0.23.2-foss-2020b

There is a newer install of scikit-learn

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 0.23.2-foss-2020b

To load the module for scikit-learn 0.23.2-foss-2020b please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load scikit-learn/0.23.2-foss-2020b

There is a GPU enabled version of this module: scikit-learn 0.23.2-fosscuda-2020b

BEAR Apps Version

2020b

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.

More Information

For more information visit the scikit-learn website.

Dependencies

This version of scikit-learn has a direct dependency on: foss/2020b Python/3.8.6-GCCcore-10.2.0 SciPy-bundle/2020.11-foss-2020b

Required By

This version of scikit-learn is a direct dependent of: Compass/20220402-foss-2020b cryoDRGN/0.3.5-foss-2020b geopandas/0.9.0-foss-2020b hyperopt/0.2.5-foss-2020b iterative-Random-Forest/0.2.5-foss-2020b mofapy2/0.5.7-foss-2020b nevergrad/0.4.3.post4-foss-2020b topaz/0.2.4-foss-2020b-PyTorch-1.7.1 TumorDecon/1.1.1-foss-2020b umap-learn/0.4.6-foss-2020b

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 15th April 2021