A. Catana

Resampling strategies for machine learning-based effort overrun risk detection: A controlled factorial study with cost-sensitive evaluation

Two overarching results are revealed: near-perfect performance under a full feature set is largely attributable to target leakage rather than a genuine predictive signal, and, under deployment, valid early-warning features degrade cost-sensitive performance relative to no resampling when SMOTE is used.