2026-10-10T03:44:55Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33975896 2026-09-23T17:34:17Z category_734 category_931 category_106 portal_5 item_type_3 month_year_09_2026

<p>Counterfactual Explanation Quality Across Both Datasets.</p> Nusrat Jahan (399039) Jannatul Maua (23120176) Uland Rozario (25099154) Hashibul Ahsan Shoaib (24386599) Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy world managerial decision integrates predictive modeling integrates deep learning human resource management customer relationship management employee attrition prediction customer churn prediction support managerial decision experimental results show aware optimization problem actionable managerial insights employee attrition dataset customer churn dataset sensitive counterfactual generation churn dataset attrition dataset support systems results indicate enabling actionable counterfactual optimization unified objective proposed model practical adoption plausible recommendations paper proposes increasingly used feature attribution explanation fidelity domain setting deliver accurate counterfactual reasoning combined multi brier score baseline approaches <p>Counterfactual Explanation Quality Across Both Datasets.</p> 2026-09-23T17:34:02Z Dataset Dataset 10.1371/journal.pone.0358643.t007 https://figshare.com/articles/dataset/_p_Counterfactual_Explanation_Quality_Across_Both_Datasets_p_/33975896 CC BY 4.0