How Data Scientists Build Interpretable ML Models with SHAP
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
Lucas and Luna explore the practical use of SHAP (SHapley Additive exPlanations) for interpreting complex machine learning models. They walk through a real-world example: a credit risk model from a mid-sized European fintech that needed regulatory compliance under GDPR. Lucas explains how SHAP values decompose a prediction into feature contributions, and why game theory provides a principled foundation. Luna questions whether SHAP is always better than simpler alternatives like LIME, and they compare trade-offs in speed, consistency, and trust. The episode includes a concrete walkthrough of a single prediction breakdown, showing how a 32-year-old applicant with a thin credit file got denied because of a specific feature interaction. They also touch on open-source tools like the SHAP Python library and how one data team at Klarna uses SHAP summaries to communicate with non-technical stakeholders. No clickbait, just a clear look at one of the most widely adopted interpretability methods in the field today. #SHAP #InterpretableML #ExplainableAI #XAI #SHAPValues #GameTheory #FeatureImportance #ModelInterpretability #CreditRiskModeling #GDPR #LIME #Klarna #DataScience #Technology #MachineLearning #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo
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