Summary

Understanding and explaining complex machine learning models such as XGBoost is necessary for ensuring transparency, trust, and effective debugging. In this chapter, you explored a range of interpretability techniques – SHAP, LIME, ELI5, XGBoost’s feature importance, and PDPs – each providing valuable insights into how the model works.

First, you saw how SHAP excels at both global and local explanations, providing a comprehensive view of how features influence the model overall, as well as detailed, instance-specific insights. Then, we looked at LIME, which offers flexible, model-agnostic explanations while focusing on individual predictions and revealing what drives each model output. Next, we covered ELI5, which simplifies global feature importance, offering intuitive explanations that make it easier to communicate key model drivers.

In addition to these tools, you used XGBoost’s built-in feature importance, which offers a clear ranking of...