Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean

1 code implementation • 27 Jan 2023 • Anton Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken

We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models.

Additive models