I'm attempting to use SHAP to automatically extract feature names that have a positive impact on my regression models. On inspection of the code I see that the bar plot, for example, determines these by taking the mean absolute SHAP values for a feature. Being an absolute value, it obviously takes the absolute impact but I want to only consider positive impacting values.
Is my intuition that I can just take the mean instead of the mean of the absolute values correct? (highly) Negative SHAP values should give a negative mean value.
Is this a good approach or am I missing some better way to do this?
I am specifically interested in features that raise the predicted value. ie. if
feature_1 lifts the predicted value by 100 and
feature_2 by 1000, I want this information to be extracted as
feature_2 has and higher impact on the output value.