In episode 94 of The Data Science Podcast with Fexingo, Lucas and Luna explore how active learning cuts labeling costs by 80 percent while maintaining model accuracy. Using a concrete example from a medical imaging startup training a rare-disease classifier, they walk through uncertainty sampling, query strategies, and the human-in-the-loop workflow. They compare pool-based versus stream-based active learning, discuss common pitfalls like distribution shift, and explain when active learning beats random sampling. If you are a data scientist looking to stretch a limited labeling budget, this episode gives you a practical framework to get started. Lucas and Luna also touch on tools like modAL, scikit-activeml, and Label Studio. No hype, just signal.

#ActiveLearning #DataScience #MachineLearning #Labeling #UncertaintySampling #HumanInTheLoop #MedicalImaging #RareDisease #modAL #scikitActivelm #LabelStudio #DistributionShift #QueryStrategy #PoolBased #StreamBased #Technology #FexingoBusiness #BusinessPodcast

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