CIROH Dev Con SHAP Values Workshop
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| Owners: | Harrison Myers |
| Type: | Resource |
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| Created: | May 15, 2025 at 2:22 p.m. (UTC) |
| Last updated: | May 27, 2025 at 3:47 p.m. (UTC) |
| Citation: | See how to cite this resource |
| Content types: | CSV Content |
| Sharing Status: | Public |
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Abstract
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
Subject Keywords
Coverage
Spatial
Temporal
| Start Date: | 01/01/1980 |
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| End Date: | 12/31/2014 |
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How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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