How Data Scientists Use SHAP Values to Explain Model Predictions

Episode 129 of The Data Science Podcast dives into SHAP values — the most widely used method for explaining individual predictions from machine learning models. Lucas and Luna walk through a concrete case: a credit-card issuer explaining why a specific applicant was denied, using SHAP to trace the contribution of each feature. They discuss the math behind Shapley values from cooperative game theory, the computational trade-offs between TreeSHAP and KernelSHAP, and common pitfalls like feature correlation. By the end, you'll understand how SHAP turns a black-box model into a transparent, auditable decision tool — and why regulators increasingly expect it. Practical, grounded, no fluff. #SHAP #ModelExplainability #XAI #DataScience #MachineLearning #InterpretableML #ShapleyValues #TreeSHAP #KernelSHAP #CreditScoring #FeatureImportance #AIEthics #ExplainableAI #Technology #DataDriven #FexingoBusiness #BusinessPodcast #TechPodcast Keep every episode free: buymeacoffee.com/fexingo