Model explainability approaches using SHAP
After reading the previous section, you have gained an understanding of SHAP and Shapley values. In this section, we will discuss various model-explainability approaches using SHAP. Data visualization is an important method to explain the working of complex algorithms. SHAP makes use of various interesting data visualization techniques to represent the approximated Shapley values to explain black-box models. So, let's discuss some of the popular visualization methods used by the SHAP framework.
Visualizations in SHAP
As mentioned previously, SHAP can be used for both the global interpretability of the model and the local interpretability of the inference data instance. Now, the values generated by the SHAP algorithm are quite difficult to understand unless we make use of intuitive visualizations. The choice of visualization depends on the choice of global interpretability or local interpretability, which we will cover in this section...