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neuralGAM  

Interpretable Neural Network Based on Generalized Additive Models
View on CRAN: Click here


Download and install neuralGAM package within the R console
Install from CRAN:
install.packages("neuralGAM")

Install from Github:
library("remotes")
install_github("cran/neuralGAM")

Install by package version:
library("remotes")
install_version("neuralGAM", "1.1.0")



Attach the package and use:
library("neuralGAM")
Maintained by
Ines Ortega-Fernandez
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-09-07
Latest Update: 2023-09-20
Description:
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), 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.
How to cite:
Ines Ortega-Fernandez (2023). neuralGAM: Interpretable Neural Network Based on Generalized Additive Models. R package version 1.1.0, https://cran.r-project.org/web/packages/neuralGAM
Previous versions and publish date:
1.0.0 (2023-09-07 17:50), 1.1.0 (2023-09-20 20:20)
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