SHAP is the most powerful Python package for understanding and debugging your machine learning models. It can be used to explain both individual predictions and trends across multiple predictions. We explore how by walking through the code and explanations for the SHAP waterfall plot, force plot, absolute mean plot, beeswarm plot and dependence plots.
SHAP course: https://adataodyssey.com/courses/shap-with-python/
XAI course: https://adataodyssey.com/courses/xai-with-python/
Newsletter signup: https://mailchi.mp/40909011987b/signup
*NOTE*: You will now get the XAI course for free if you sign up (not the SHAP course)
Read the companion article (no-paywall link):
https://medium.com/data-science/shap-for-categorical-features-7c63e6a554ea?sk=2eca9ff9d28d1c8bfde82f6784bdba19
SHAP for Categorical Features (no-paywall link): https://medium.com/data-science/shap-for-categorical-features-7c63e6a554ea?sk=2eca9ff9d28d1c8bfde82f6784bdba19
Medium: https://conorosullyds.medium.com/
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Mastodon: https://sigmoid.social/@conorosully
Website: https://adataodyssey.com/
