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Uncertainty quantification in automated valuation models with locally weighted conformal prediction

By Anders Hjort and others at
LogoUniversity of Oslo
and
LogoNorth Carolina State University
Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncertainty. Conformal Prediction (CP) is a model-agnostic framework for constructing confidence sets around machine... Show more
December 11, 2023
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Uncertainty quantification in automated valuation models with locally weighted conformal prediction
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