Understanding The Shapley Value

Shapley Value is one of the most prominent ways of dividing up the value of a society, the productive value of some, set of individuals among its members. The Shapley Value is, is based on Lloyd Shapley's idea that members should basically be receiving things which are proportional to their marginal contributions. So, basically we look at what, what does a person add when we add them to a group. and they should be getting something that reflects their added value to the society.
In machine learning, the contribution of each feature is how much it helps the model learn and separate each class or reduce erro.

[@AradAshrafi]

one of the best explanations of Shapley values for an ML person. Thanks a lot

[@NeverHadMakingsOfAVarsityAthle]

Hey! Thanks for the fantastic content :) I'm trying to understand the additivity axiom a bit better. Is this axiom the main reason why Shapley values for machine learning forecast can just be added up for one feature over many different predictions? Let's say we can have predictions for two different days in a time series and each time we calculate the shapley value for the price value. Does the additivity axiom then imply that I can add up the Shapley values for price for these two predictions (assuming they are independent) to make a statement about the importance of price over multiple predictions?

[@michalbogacz202]

It was great!!!

[@lingfengzhang2943]

Thanks! It's very clear

[@abdelrahmanwaelhelaly1871]

Thank you so much

[@zhaobryan4441]

super super clear!

[@nunaworship]

Can you please share the link for the books you recommended!

[@SrDazz]

thanks!

[@paaabl0.]

Shapley values are great, but not gonna help you much with complex non-linear patterns, especially in terms of global feature importance