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Glass Box Neural Networks

Started ‎06-10-2022 by
Modified ‎06-10-2022 by
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Neural network models are typically described as “black boxes” because their inner workings are not easy to understand. We propose that, since a neural network model that accurately predicts its target variable is a good representation of the training data, the output of the model may be recast as a target variable and subjected to standard regression algorithms to “explain” it as a response variable. Thus, the “black box” of the internal mechanism is transformed into a “glass box” that facilitates understanding of the underlying model.




I often use that technique as well. Works good !

Since VIYA, I also use :

  • Partial Dependence Plots (PD-plots)
  • Individual Conditional Expectation Plots (ICE-plots)
  • Local Interpretable Model-agnostic Explanations (LIME) visualisations
  • Shapley values (shapleyExplainer action)

Much info on the above can be found when Googling ( include SAS key-word when you are searching !! )



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‎06-10-2022 03:26 PM
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