Uncertainty Quantification (1): Enter Conformal Predictors скачать в хорошем качестве

Uncertainty Quantification (1): Enter Conformal Predictors

Channel's GitHub page hosting Jupyter Notebook: https://github.com/mtorabirad/MLBoost In this video, we explore the concept of uncertainty quantification in machine learning predictions and introduce a powerful class of methods called Conformal Predictors. Learn why relying solely on point predictions can be problematic and discover how quantifying uncertainty empowers informed decision-making. We delve into the concept of prediction intervals with attached probabilistic statements, discuss the importance of coverage validity and efficiency, and explore the challenges of achieving finite-sample validity. We also highlight the desirable properties of model-agnostic and distribution-free interval predictors. Join us on this journey to understand how Conformal Predictors provide model-agnostic, distribution-free intervals with finite-sample validity. Enhance your understanding of uncertainty quantification and expand your UQ methods repertoire. Keywords: uncertainty quantification, Conformal Predictors, prediction intervals, coverage validity, efficiency in prediction intervals, finite-sample validity, model-agnostic interval predictors, distribution-free interval predictors, machine learning predictions, informed decision-making, point predictions, error probability, linear regression, neural network 0:00 - Why Uncertainty Quantification 0:20 - Limitations of Point Predictions 1:20 - Going Beyond Point Predictions 1:40 - Empowering Decision-Making with Uncertainty Quantification 2:02 - Predicting Intervals with Probabilistic Statements 2:43 - Embracing Error and Quantifying Its Probability 3:01 - Importance of Coverage Validity 3:40 - Verifying Claimed Coverage Validity 3:52 - Finite-sample Validity 4:33 - The Role of Efficiency in Reliable Prediction Intervals 5:15 - Beyond Validity and Efficiency: Model-Agnostic and Distribution-Free Interval Predictors 5:54 - The Desirable Properties of Interval Predictors 6:30 - How Conformal Predictors Work