shapr-package: shapr: Prediction Explanation with Dependence-Aware Shapley...

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

shapr: Prediction Explanation with Dependence-Aware Shapley Values

Description

Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Author(s)

Maintainer: Martin Jullum [e-mail] (ORCID)

Authors:

• Lars Henry Berge Olsen [e-mail] (ORCID)

• Annabelle Redelmeier [e-mail]

• Jon Lachmann [e-mail] (ORCID)

• Nikolai Sellereite [e-mail] (ORCID)

Other contributors:

• Anders Løland [e-mail] [contributor]

• Jens Christian Wahl [e-mail] [contributor]

• Camilla Lingjærde [contributor]

• Norsk Regnesentral [copyright holder, funder]

See Also

Useful links:

• https://norskregnesentral.github.io/shapr/

• https://github.com/NorskRegnesentral/shapr/

• Report bugs at https://github.com/NorskRegnesentral/shapr/issues

shapr documentation built on April 4, 2025, 12:18 a.m.