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A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.

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Difference between shap values and feature contributions

I always found both concepts a bit confusing since they are quite similar. Would someone provide clear example where to apply each? Shap values ref: https://towardsdatascience.com/explain-your-model- …
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