Ever wondered how to interpret your machine learning models? We explain a powerful interpretability technique for machine learning models: Shapley Values. They can be used to explain any model. We show a simple example code of how they work, and then explain the theory behind them.
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Outline:
00:00 Interpretability in AI
01:02 AssemblyAI (Sponsor)
02:23 Simple example
03:51 Code example: SHAP
05:17 Shapley Values explained
07:59 Shortcomings of Shapley Values

Demo for SHAP on LLaMA 2 LLM: https://drive.google.com/drive/folder...
Keep in mind that you need to have the resources to run LLaMA 2. If not, try out the “gpt2” model in the code. You can find simple examples here: https://shap.readthedocs.io/en/latest/ (see e.g., “Text examples”)

“Interpretable Machine Learning” by C. Molnar: https://christophm.github.io/interpre...

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Video editing: Nils Trost