We have the following table the output of a model f on an example using subsets of the variable. What is the Shapley value for s_BP?

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Refer to the lesson Combining importances.  We know that we compute our Shapley value by taking the prediction value of all features and subtracting it by the prediction values of the features that don't contain our desired value. We do this until we take the prediction value of the desired feature minus the prediction value of the empty set.   We compute the Shapley value for s_BP in the following way:   {d_BP, s_BP} - {d_BP} = (0.65)  - (0.6) = 0.05  {s_BP} - {} = (0.7) - (0.5) = 0.2  Once we have obtained all of our values, we sum them up altogether, then divide by the number of features we have. In this case, we have 2 total features, so we divide by 2.   Calculate the importance of s_BP:  ( (0.05) + (0.2) ) / 2  (0.25) / 2  The shapley value for s_BP is: 0.125

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