Conformal prediction is a statistical framework that provides a way to quantify the uncertainty of predictions made by machine learning models. It offers a method to produce prediction intervals (or sets) that are valid under minimal assumptions about the model and the underlying data distribution. The key idea behind conformal prediction is to leverage the notion of "conformity" or how well new data points fit into the distribution of previously observed data.
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