CIROH Dev Con SHAP Values Workshop

This workshop introduces participants to SHAP (SHapley Additive exPlanations) values (Lundberg & Lee, 2017), a useful tool for interpreting machine learning models by analyzing feature importance. Participants will learn the fundamentals of SHAP values and their practical applications to understand how individual features influence predictions in regression and classification models. Using Python libraries, we will walk through hands-on examples to explore how SHAP values can enhance model transparency, provide actionable insights, and build trust in machine learning predictions, with a focus on real-world applications in water resources.

Learning Outcomes:

Understand SHAP values and their role in interpreting machine learning models.
How to implement SHAP values in Python
How to post-process SHAP tool output to create visualizations (e.g., bee swarm plot)
How qualitative evaluation of SHAP values can yield process insights for the system being modeled