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.
