NAM
Neural Additive Model
**Neural Additive Models (NAMs)** make restrictions on the structure of neural networks, which yields a family of models that are inherently interpretable while suffering little loss in prediction accuracy when applied to tabular data. Methodologically, NAMs belong to a larger model family called Generalized Additive Models (GAMs). 

NAMs learn a linear combination of networks that each attend to a single input feature: each $f\_{i}$ in the traditional GAM formulationis parametrized by a neural network. These networks are trained jointly using backpropagation and can learn arbitrarily complex shape functions. Interpreting NAMs is easy as the impact of a feature on the prediction does not rely on the other features and can be understood by visualizing its corresponding shape function (e.g., plotting $f\_{i}\left(x\_{i}\right)$ vs. $x\_{i}$).
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://paperswithcode.com/paper/neural-additive-models-interpretable-machine
Neural Additive Models: Interpretable Machine Learning with Neural Nets