Explainability Report
Model explained: xgboost
Global SHAP Feature Importance
| feature | mean_abs_shap |
|---|---|
| categorical__OverTime_No | 0.648109 |
| numeric__StockOptionLevel | 0.476637 |
| numeric__MonthlyIncome | 0.435742 |
| numeric__Age | 0.406575 |
| numeric__NumCompaniesWorked | 0.341085 |
| numeric__YearsWithCurrManager | 0.337918 |
| numeric__EnvironmentSatisfaction | 0.299383 |
| numeric__RelationshipSatisfaction | 0.291014 |
| numeric__DistanceFromHome | 0.275919 |
| numeric__JobSatisfaction | 0.261066 |
| numeric__WorkLifeBalance | 0.217149 |
| numeric__DailyRate | 0.211032 |
| categorical__BusinessTravel_Travel_Frequently | 0.208189 |
| numeric__YearsSinceLastPromotion | 0.20361 |
| categorical__JobRole_Research Scientist | 0.20019 |
| numeric__HourlyRate | 0.158579 |
| numeric__JobInvolvement | 0.143332 |
| numeric__TotalWorkingYears | 0.143067 |
| categorical__Department_Research & Development | 0.140875 |
| categorical__JobRole_Laboratory Technician | 0.118529 |
| categorical__Gender_Female | 0.114629 |
| categorical__JobRole_Sales Executive | 0.112518 |
| numeric__Education | 0.109931 |
| numeric__YearsAtCompany | 0.106411 |
| numeric__PercentSalaryHike | 0.104101 |
Notes
- SHAP values explain feature contributions to model predictions.
- Global importance is computed as mean absolute SHAP value.
- Local explanations are generated for representative validation samples.