SHAP values interpretation for clasification

I'm trying to understand how SHAP values are calculated for Classification. As far as I understand for each feature the SHAP values are calculated by:

$$ \phi_i = \sum_{S \subseteq F \setminus {i}} \frac{|S|!(|F|-|S|-1)!}{|F|!} \left[ f_{S\cup{i}} (x_{S\cup{i}})-f_S(x_S) \right] $$

For regression it makes sense that for three features ${A,B,C}$ each feature has a value. The prediction for one row might be ${A,B,C} = 50$. Then all possible coalitions are calculated with and without the feature to find the marginal contribution (Shap values) for each feature for that particular prediction of 50. Now my question is, how do I do the same for e.g., a binary classification scenario? If the prediction of the set ${A,B,C} = 1$, then how does it work?

I would really appreciate someone making an example for finding SHAP values given a binary classification problem as I'm really stuck...