Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support

no code implementations • 14 Dec 2020 • Adedolapo Okanlawon, Huichen Yang, Avishek Bose, William Hsu, Dan Andresen, Mohammed Tanash

We present a supervised learning model trained on a Simple Linux Utility for Resource Management (Slurm) data set of HPC jobs using three different techniques for selecting features: linear regression, lasso, and ridge regression.

feature selection Management +1