LightGBM

PREDICTION OF INDUSTRIAL EQUIPMENT CONDITION USING COST-SENSITIVE APPROACHES AND CLASSIFICATION THRESHOLD OPTIMIZATION

This paper presents a comprehensive study on the application of modern machine learning methods for predictive maintenance based on the open AI4I Predictive Maintenance dataset. The primary goal of the research is to develop and compare both binary and multiclass classification models that enable not only the prediction of machine failures but also the identification of specific failure types.

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