When Does Cost-Sensitive Learning Help in Predictive Maintenance? A Cross-Dataset Benchmark Study — Code and Data

Complete experimental code, per-fold raw scores, and aggregated result tables for the paper When Does Cost-Sensitive Learning Help in Predictive Maintenance? A Cross-Dataset Benchmark Study (Neural Computing and Applications, under review, 2026). The study benchmarks nine learners under one identical protocol (10-fold cross-validation repeated three times) across three predictive-maintenance datasets spanning a difficulty gradient — MetroPT-3 (separable), AI4I-2020 (moderate), and SCANIA Component X (hard) — plus CWRU as a saturated cautionary case, using a Normalised Cost Savings (NCS) metric comparable across datasets. The archive reproduces every table and figure in the paper.