2026-10-07T00:49:15Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33857824 2026-09-16T15:26:19Z category_29890 category_29173 item_type_3 month_year_09_2026

Code and Data for "Null importance: Disentangling relevance for interpretable machine learning." Kris Sankaran (15347602) Garvesh Raskutti (6082160) Statistical data science Machine learning not elsewhere classified Interpretability feature-importance null importance <p dir="ltr">The code here mirrors that in our GitHub repository (https://github.com/krisrs1128/null_importance). Each case study is a subdirectory of the <code>case_studies</code> folder. The READMEs describe the commands that are used to replicate the analysis and visualization. Under case studies, the <code>environment.yml</code> file defines the conda environment "interpretability_case_studies" which describes all the all the necessary packages to replicate the results.</p><p dir="ltr">The real and simulated data used as inputs for these case studies are further subdirectories, e.g., <code>sweep_tabular/data</code>. The experiments are run using Hydra, and the associated configuration files are stored under <code>conf/</code>. The CSV files used in all the figures and the figures themselves are stored under <code>results</code></p> 2026-09-16T15:26:19Z Dataset Dataset 10.6084/m9.figshare.33857824.v1 https://figshare.com/articles/dataset/Code_and_Data_for_Null_importance_Disentangling_relevance_for_interpretable_machine_learning_/33857824 CC BY 4.0