SHAP

Explainable AI and robust forecasting of global salary trends: Addressing data drift and unseen categories with tree-based models

This article studies salary prediction under distributional drift using explainable boosting models and hybrid forecasting.  We integrate unseen-aware feature engineering, robust objectives, SHAP-based interpretability, drift detection, and time-series forecasting (Prophet/SARIMAX) on multi-year data (2020–2024), and report a comprehensive evaluation aligned with typical MMC guidelines.  Modern salary data are heterogeneous, heavy-tailed, and non-stationary.  Therefore we combine robust tree-based learners with drift monitoring and explainable forecasting to prioritize

SHAP-BASED EVALUATION OF FEATURE IMPORTANCE IN BGP ANOMALY DETECTION MODELS

The classification of Border Gateway Protocol (BGP) anomalies is essential for maintaining Internet stability and security, as such anomalies can impair network functionality and reliability. Previous studies has examined the impact of key features on anomaly detection; however, current methodologies frequently demonstrate high computational costs, complexity, and usage challenges.