neuralGAM: Interpretable Neural Network Based on Generalized Additive Models

neuralGAM is a neural network framework based on Generalized Additive Models, which trains a different neural network to estimate the contribution of each feature to the response variable.

The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.

neuralGAM is available as an R Software package at the CRAN. A complete description of the R Package is available here.