In this episode, we dive into two powerful machine learning techniques for uncertainty quantification: Conformal Prediction (CP) and Bayesian Prediction (BP). CP ensures reliable confidence intervals for predictions, making it highly interpretable and model-agnostic. On the other hand, BP leverages Bayes’ Theorem to continuously refine predictions with new data, prioritizing adaptability and probabilistic reasoning.

We break down the strengths and weaknesses of each approach, explore real-world applications in fintech and healthcare, and discuss when startups might benefit from combining both methods. Whether you're optimizing AI for reliability, dynamic learning, or interpretability, this episode will help you make an informed choice.

Send us a text

Support the show

Podcast:
https://kabir.buzzsprout.com


YouTube:
https://www.youtube.com/@kabirtechdives

Please subscribe and share.