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What do Shapley values represent?
Essentially, the Shapley value is the average expected marginal contribution of one player after all possible combinations have been considered. Shapley value helps to determine a payoff for all of the players when each player might have contributed more or less than the others.
Do Shap values add to 1?
1 Answer. Almost yes. There are a few caveats regarding directly interpreting the scaled SHAP values, as the percentage contributions of our final classification prediction for a single observation. Raw SHAP values for classification tasks are often shown as additive contribution in the log-odds domain.
Which is the correct interpretation of the Shapley value?
Be careful to interpret the Shapley value correctly: The Shapley value is the average contribution of a feature value to the prediction in different coalitions. The Shapley value is NOT the difference in prediction when we would remove the feature from the model.
How are Shapley values used in predictive models?
But computing Shapley values for model features is not entirely straightforward, because features in a model do not behave the same way as workers in a team. Specifically, a predictive model cannot typically handle one of its input features being simply removed in order to test how its output changes as a result.
What does the sum of Shapley values yield?
The sum of Shapley values yields the difference of actual and average prediction (-2108). Be careful to interpret the Shapley value correctly: The Shapley value is the average contribution of a feature value to the prediction in different coalitions.
What are the disadvantages of using Shapley values?
Another disadvantage is that you need access to the data if you want to calculate the Shapley value for a new data instance. It is not sufficient to access the prediction function because you need the data to replace parts of the instance of interest with values from randomly drawn instances of the data.