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 the method described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying Python wrapper (shaprpy) is available on GitHub.
Package details |
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Maintainer | |
License | MIT + file LICENSE |
Version | 0.2.3.9200 |
URL | https://norskregnesentral.github.io/shapr/ https://github.com/NorskRegnesentral/shapr/ |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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