84Stakeholders ask which features drive your model. Why is feature importance misleading, and what do you use instead?▼mediumDatabricksMetaAmazon2 replies◆ premiumThe built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will make decisions on that bar chart. Here is why default importance lies and what a careful answer reports instead.Open full answer →
120Compare SHAP and LIME for explaining model predictions. What does SHAP guarantee that LIME does not?▼mediumGoogleMicrosoftAmazon2 replies◆ premiumBoth explain a single prediction by attributing it to features, but only one rests on a uniqueness theorem. The signal is knowing SHAP's game-theory guarantees, LIME's instability, and when each is the right tool. Here is the answer.Open full answer →
11What is the difference between explainability and interpretability, and how do you explain a model's decision?▼mediumGoogleMicrosoftIBM2 replies○ sign inRegulators and enterprises increasingly demand explanations for AI decisions, and the two terms get used loosely. The signal is separating inherently interpretable models from post-hoc explanations and naming the right technique for the stakes.Open full answer →
