Machine learning algorithms have achieved remarkable triumphs in recent years, but their decision-making processes often remain a mystery. This lack of transparency, often referred to as the "black box" problem, poses challenges for trust, integration, and interpretation. Explainability in machine learning aims to shed light on these opaque functio