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What is Shap value in ML?
SHAP values interpret the impact of having a certain value for a given feature in comparison to the prediction we’d make if that feature took some baseline value. An example is helpful, and we’ll continue the soccer/football example from the permutation importance and partial dependence plots lessons.
How is Shapley calculated?
The Shapley value is computed by taking the average of difference from all combinations. Essentially, the Shapley value is the average marginal contribution of a feature considering all possible combinations.
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 to interpret Shapley force plot for..?
From the example plot, you can draw the following interpretation: “sample n°4100 is predicted to be -2.92, which is much lower than the average predicted value (~0.643), mostly due to the specific values of features PEEP_min (5), Fi02_100_max (50), etc., and although day_2_balance is 532”.
How are Shapley values visualized in Python Shap?
But with the Python shap package comes a different visualization: You can visualize feature attributions such as Shapley values as “forces”. Each feature value is a force that either increases or decreases the prediction. The prediction starts from the baseline. The baseline for Shapley values is the average of all predictions.
How to plot a summary plot using Shap?
In order to understand the variable importance along with their direction of impact one can plot a summary plot using shap python library. This plot’s x-axis illustrates the shap values (-ve to +ve) and the y-axis indicates the features (variables). The colour bar indicates the impact.
https://www.youtube.com/watch?v=ylytZegK–I