Exercise: SHAP Visualization and Analysis

Exploring feature interactions with SHAP values

In this exercise, you’ll become more familiar with using SHAP values to provide visibility into the workings of a model. First, we’ll take an alternate look at the interaction between Features 3 and 5, and then use SHAP values to calculate feature importances similar to what we did with a random forest model in the chapter “Decision Trees and Random Forests.” Finally, we’ll see how model outputs can be obtained from SHAP values, taking advantage of their additive property:

1. Given the preliminary steps accomplished in this section already, we can take another look at the interaction between Features 3 and 5, the two most important features of the synthetic dataset. Look at the SHAP values of Feature 5, colored by those of Feature 3:

   shap.plots.scatter(shap_values[:,'Feature 5'], color=shap_values[:,'Feature 3'])
   

   The resulting plot should look like this: