How Data Scientists Use Quantile Regression for Uncertainty

Episode 152 of The Data Science Podcast dives into quantile regression, a technique that gives data scientists a fuller picture of uncertainty than standard mean-focused models. Lucas and Luna explore how predicting the 90th percentile instead of the average can transform decision-making in finance, healthcare, and logistics. They walk through a concrete example from e-commerce, comparing quantile regression to ordinary least squares, and discuss practical implementation using libraries like LightGBM and scikit-learn. The conversation covers key concepts such as pinball loss, interval coverage, and how to explain these models to non-technical stakeholders. With August 2026’s data-science landscape in mind, they also touch on the growing role of uncertainty quantification in regulated industries. By the end, listeners understand why quantile regression belongs in every data scientist’s toolkit and how it can reveal risks that averages hide. #QuantileRegression #UncertaintyQuantification #MachineLearning #DataScience #PredictiveAnalytics #Finance #Healthcare #Ecommerce #LightGBM #ScikitLearn #PinballLoss #RiskManagement #Technology #BusinessAnalytics #DataDriven #FexingoBusiness #BusinessPodcast #DataSciencePodcast Keep every episode free: buymeacoffee.com/fexingo