shap

The built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will base decisions on that bar chart. Here is why default importance misleads and what a careful answer reports instead.

Both explain one prediction by crediting features, but only one is backed by a uniqueness theorem. What interviewers reward is knowing SHAP's game-theory guarantees, LIME's instability, and when each fits. Here is the answer.

Regulators and enterprises increasingly require explanations for AI decisions, and the two terms get thrown around loosely. What lands is separating inherently interpretable models from post-hoc explanations and picking the right technique for the stakes.