New R package neuralGAM offers interpretable deep learning models

A new R package named neuralGAM has been developed to address the "black-box" problem in neural networks. This package implements a topology based on Generalized Additive Models, allowing for the estimation of each feature's contribution to the output. The resulting deep learning models are both accurate and interpretable, with flexibility in neural network architecture. AI

IMPACT Provides a new tool for researchers and practitioners seeking more interpretable deep learning models.

RANK_REASON The cluster describes a new R package for fitting a specific type of neural network, detailed in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• deep learning
• Generalized Additive Models
• Ines Ortega-Fernandez
• Neural Networks
• R

• paper
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