I have been using DeepExplainer (DE) to obtain the approximate SHAP values for my MLP model. I am following https://github.com/slundberg/shap and DE's performance is very high in terms of computation time.

Now I'd like learn the logic behind DE more. From the paper (https://arxiv.org/pdf/1704.02685.pdf) it is not clear to me how SHAP values are gotten. I see that a background sample set is given and an expected model output is calculated based on this data and the difference is calculated with the current model's output. This difference is the sum of the SHAP values. However, I don't understand how each contribution is obtained? Could you give an explanation with simple terms?

Many thanks in advance!


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