When compared to CatBoost object importance, MVS-A beats it on 16 datasets, achieving mean performance increases of 12%, 3%, and 5% for relative precision, enrichment factor and BEDROC respectively. This highlights that MVS-A is more effective at assessing sample influence than the previous state-of-the-art for GBM algorithms, since it detects high fidelity data more efficiently.