Research Article
XGBoost-Based Employee Attrition Prediction with SHAP Explainability: A Comparative Study of Supervised Classification Algorithms on the IBM HR Analytics Dataset
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 99 |
| Published: April 2026 |
| Authors: Anurag Bodkhe, Sahil Jirapure, Ujjwal Garud, Shrinivas Bhore |
10.5120/ijca1f36c9153a77
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Anurag Bodkhe, Sahil Jirapure, Ujjwal Garud, Shrinivas Bhore . XGBoost-Based Employee Attrition Prediction with SHAP Explainability: A Comparative Study of Supervised Classification Algorithms on the IBM HR Analytics Dataset. International Journal of Computer Applications. 187, 99 (April 2026), 7-11. DOI=10.5120/ijca1f36c9153a77
@article{ 10.5120/ijca1f36c9153a77,
author = { Anurag Bodkhe,Sahil Jirapure,Ujjwal Garud,Shrinivas Bhore },
title = { XGBoost-Based Employee Attrition Prediction with SHAP Explainability: A Comparative Study of Supervised Classification Algorithms on the IBM HR Analytics Dataset },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 99 },
pages = { 7-11 },
doi = { 10.5120/ijca1f36c9153a77 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Anurag Bodkhe
%A Sahil Jirapure
%A Ujjwal Garud
%A Shrinivas Bhore
%T XGBoost-Based Employee Attrition Prediction with SHAP Explainability: A Comparative Study of Supervised Classification Algorithms on the IBM HR Analytics Dataset%T
%J International Journal of Computer Applications
%V 187
%N 99
%P 7-11
%R 10.5120/ijca1f36c9153a77
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Employee attrition remains one of the most consequential workforce challenges facing contemporary organizations, with replacement costs estimated between 50% and 200% of an affected employee's annual compensation. This paper presents the design, implementation, and empirical evaluation of an Employee Attrition Prediction System (EAPS) built on supervised machine learning techniques applied to the IBM HR Analytics dataset comprising 1,470 employee records and 35 workforce attributes. Four classification algorithms—Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost—are systematically trained, tuned, and evaluated under realistic class-imbalance conditions using the Synthetic Minority Oversampling Technique (SMOTE). Three domain-informed engineered features are introduced: Compensation Ratio, Tenure per Job, and Years Without Change. Experimental results demonstrate that XGBoost achieves superior performance across all five evaluation metrics, attaining 97.2% accuracy, 96.8% precision, 95.4% recall, a macro F1 score of 96.1%, and an AUC-ROC of 0.991. A modular six-component system architecture is proposed, culminating in an HR decision-support dashboard leveraging SHAP (SHapley Additive exPlanations) values for individualized, interpretable attrition risk assessments.
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