Explain what your model learned using SHAP values, with global and local interpretation done correctly.
## CONTEXT A model that predicts well but cannot be explained is hard to trust, debug, or deploy in regulated settings. Interpretability tools like SHAP attribute each prediction to its features, but they are easy to misread: confusing global importance with causation, ignoring feature correlation, or over-trusting a…
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