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, BEAR Cloud VMs, and CaStLeS VMs):

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

BEAR Apps Version

2019b

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