{"id":7868,"date":"2023-10-06T15:02:10","date_gmt":"2023-10-06T23:02:10","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=7868"},"modified":"2025-04-24T17:05:43","modified_gmt":"2025-04-24T17:05:43","slug":"integrating-comet-with-shap-values","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/integrating-comet-with-shap-values\/","title":{"rendered":"Integrating Comet with Shap Values"},"content":{"rendered":"\n
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Photo by Christin Hume<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

Sometimes when you build a Machine Learning model, the results produced differ from those expected, despite having appropriately taken all the necessary precautions (feature selection, model optimization, and so on). In these cases, you move on to the troubleshooting phase<\/strong>, where, in addition to looking for any errors in the code, you can resort to so-called r<\/strong>everse engineering<\/strong>. Reverse engineering indicates starting with the results, and working backwards to try to understand how these results were produced.<\/p>\n

There are several techniques for performing reverse engineering in the Machine Learning sector. In this article, I describe one, based on the calculation of the Shapley value<\/strong>, a metric that describes the contribution of each input feature to an algorithm in producing the final result.<\/p>\n

There is a Python library that implements the Shapley value calculation, and also produces some interesting graphs. In this article I show how to use this library and how to integrate it into Comet.<\/p>\n

Find the official documentation here.<\/a><\/p>\n

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The article is organized as follows:<\/p>\n