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This tutorial provides a gentle introduction to conformal prediction and distribution-free uncertainty quantification. It explains how to generate finite sample confidence intervals that work for any model and dataset with low computational cost. The key concept is to obtain not only a point prediction from a neural network but also a prediction interval guaranteeing the desired output with high probability. The techniques discussed in the tutorial work in a black box manner, requiring no model analysis or retraining.
