Contents
How does Python calculate Shapley value?
This is python code to compute a Shapley Value of a characteristic form game .
- Run. python2 shapley.py
- Input File Format. N = n. v(1),…,v(1,2,…, n)
- Files. TestCases/divide_dollar file has been provided which is for the Divide the Dollar version 2 game from Prof. Y.
How is SHAP value calculated?
The idea is that: the sum of the weights of all the marginal contributions to 1-feature-models should equal the sum of the weights of all the marginal contributions to 2-feature-models and so on… In other words, the sum of all the weights on the same “row” should equal the sum of all the weights on any other “row”.
When did I first hear about Shapley values?
The first time I heard about Shapley values was when I was reading up on model interpretability. I came across SHAP, a framework for better understanding why your machine learning model behaves the way it does.
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.
What is the average contribution of a Shapley value?
The Shapley value is the average marginal contribution of a feature value across all possible coalitions. All clear now? In the following figure we evaluate the contribution of the cat-banned feature value when it is added to a coalition of park-nearby and area-50.
How are Shapley values used in machine learning?
In machine learning the participants are the features of your input and the collective payout is the model prediction. In that context Shapley values are used to calculate how much each individual feature contributes to the model output. How are Shapley values calculated? Most of the time you tend to see this equation across literature: