eli5 and SHAP for interpreting XGBoost results

Question

I have used XGBoost algorithm tried both eli5 and SHAP to interpret the results of the regression. I got some contradictory results, screenshots below.

and

I do not entirely understand the difference between eli5 and SHAP and I would like to find out which interpretation to rely more on. I would appreciate suggestions and insights into that.

Answer 1

It's probably impossible to say that one is more reliable than the other; it depends on what you're looking for. You could ask over at stats.SE or datascience.SE for some more detail about how eli5 and shap produce their valuations. It appears that eli5.show_weights is just delegating to xgboost's internal feature importances based on gain (by default), weight, or cover.

All that said, these aren't contradictory. Both the shap plot and the eli5 weights suggest that chassis_1 is the more important variable: it has larger (in absolute values) shap values as well as a higher importance score.