CIROH Dev Con SHAP Values Workshop


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Owners: Harrison Myers
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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)
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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

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Continental United States
North Latitude
49.7245°
East Longitude
-66.2695°
South Latitude
24.6870°
West Longitude
-124.9805°

Temporal

Start Date: 01/01/1980
End Date: 12/31/2014
Leaflet Map data © OpenStreetMap contributors

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How to Cite

Myers, H. (2025). CIROH Dev Con SHAP Values Workshop, HydroShare, http://www.hydroshare.org/resource/504107925379464dab88ff5780aefb5a

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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