All Times ET
Keywords: Uncertainty Quantification, PDF Calibration
Uncertainty estimates from many machine learning methods do not fulfill the statistical definition of a probability density function (PDF).Previous works have used the global distribution of Probability Integral Transform (PIT) values to re-calibrate PDFs, but offsetting inaccuracies in different regions of feature space can limit the efficacy of those methods. We leverage a recently developed technique that characterizes the local PIT distribution at any location in feature space to perform a local re-calibration of PDFs. Though we focus on an example from astrophysics, photometric redshifts, our method can produce PDFs which are calibrated at all locations in feature space for any use case.