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inesortega
neuralGAM:Interpretable Neural Network Based on Generalized Additive Models
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.
Maintained by Ines Ortega-Fernandez. Last updated 6 months ago.
deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai
11.0 match 2 stars 5.44 score 40 scripts
russellpierce
naptime:A Flexible and Robust Sys.sleep() Replacement
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.
Maintained by Russell S. Pierce. Last updated 7 months ago.
1.7 match 9 stars 5.21 score 12 scripts 1 dependents