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New R package neuralGAM offers interpretable deep learning models

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New R package neuralGAM offers interpretable deep learning models

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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]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ines Ortega-Fernandez, Marta Sestelo ·

    neuralGAM: An R Package for Fitting Generalized Additive Neural Networks

    arXiv:2505.08610v2 Announce Type: replace Abstract: Nowadays, Neural Networks are considered one of the most effective methods for various tasks such as anomaly detection, computer-aided disease detection, or natural language processing. However, these networks suffer from the ``…

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