{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Build Interpretable ML Models with SHAP","slug":"how-data-scientists-build-interpretable-ml-models-with-shap","published_at":"2026-07-10T08:58:30+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-interpretable-ml-models-with-shap","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0101.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0101.mp3","summary":"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","meta_description":"Lucas and Luna explore the practical use of SHAP (SHapley Additive exPlanations) for interpreting complex machine learning models. They walk through a rea…","key_points":[],"chapters":[],"topics":[],"duration_seconds":732,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-build-interpretable-ml-models-with-shap/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-interpretable-ml-models-with-shap.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}