SHAP is one of the most used model interpretation technique in Machine Learning. It decomposes predictions into additive contributions of the features in a fair way. For tree-based methods, the fast TreeSHAP algorithm exists. For general models, one has to resort to computationally expensive Monte-Carlo sampling or the faster Kernel SHAP algorithm. Kernel SHAP uses a regression trick to get the SHAP values of an observation with a comparably small number of calls to the predict function of the model. Still, it is much slower than TreeSHAP.
Read on to see how to do this in both R and Python. With libraries the way they are, the code is very similar and the results are basically the same.