How Data Teams Use Shapley Values to Explain Model Predictions
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In episode 169 of The Data Science Podcast, Lucas and Luna explore how data scientists use Shapley values to explain individual model predictions. They break down the cooperative game theory behind the method, walk through a real-world example from credit lending, and discuss the practical trade-offs between accuracy and interpretability. The hosts explain why Shapley values are the gold standard for feature attribution, how they differ from simpler methods like LIME, and how modern tools like SHAP make them accessible. They also touch on the computational challenges of exact Shapley values and the approximations that make them feasible in big data environments. By the end, listeners will understand not just what Shapley values are, but how to use them to build trust in machine learning models and communicate model behavior to stakeholders. A light-hearted donation plug supports the ad-free show. #ShapleyValues #ModelExplainability #MachineLearning #DataScience #FeatureAttribution #SHAP #InterpretableAI #CreditLending #CooperativeGameTheory #DataSciencePodcast #FexingoBusiness #BusinessPodcast #Technology #Analytics #Python #DataEthics #ModelTrust #AIExplanations Keep every episode free: buymeacoffee.com/fexingo
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