iterative-Random-Forest 0.2.5-foss-2020b

Uses Iterative Random Forests to detect predictive and stable high-order interactions, PNAS https://www.pnas.org/content/115/8/1943

Accessing iterative-Random-Forest 0.2.5-foss-2020b

To load the module for iterative-Random-Forest 0.2.5-foss-2020b please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load iterative-Random-Forest/0.2.5-foss-2020b

BEAR Apps Version

2020b

Architectures

EL8-cascadelake — EL8-haswell

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 and CaStLeS Cloud 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.

Extensions

  • iterative-Random-Forest-0.2.5
  • py4j 0.10.9
  • pydotplus 2.0.2
  • pyfpgrowth 1.0
  • pyspark 3.1.2

More Information

For more information visit the iterative-Random-Forest website.

Dependencies

This version of iterative-Random-Forest has a direct dependency on: foss/2020b IPython/7.18.1-GCCcore-10.2.0 JupyterLab/2.2.8-GCCcore-10.2.0 matplotlib/3.3.3-foss-2020b Python/3.8.6-GCCcore-10.2.0 PyYAML/5.3.1-GCCcore-10.2.0 PyZMQ/22.1.0-GCCcore-10.2.0 scikit-learn/0.23.2-foss-2020b SciPy-bundle/2020.11-foss-2020b

Last modified on 25th August 2021