Interpretable Gradient Boosted Modeling of Employee Attrition: A SHAP-Based Framework for HR Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines employee attrition using interpretable machine learning techniques, with a focus on enhancing strategic decision-making in human resource management. Three models—Logistic Regression, Random Forest, and Gradient Boosted Trees (GBT)—were evaluated, with GBT selected for its compatibility with SHAP (SHapley Additive exPlanations). SHAP was used to decompose the influence of variables such as Monthly Income, Over Time, and Job Satisfaction. The findings validate key HR theories, including Herzberg’s Two-Factor Theory and the Job Embeddedness framework, offering both predictive performance and theoretical alignment. The proposed model functions as a decision-support tool, providing actionable insights for HR professionals while contributing to the advancement of Management Technology through explainable AI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it