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# Plot the SHAP values for the first instance in the test data shap.force_plot(explainer.expected_value, shap_values[0,:], X_test.iloc[0,:], matplotlib=True) interpretable machine learning with python pdf download
Identifying the root cause of errors becomes much faster when you can see which features led to a wrong prediction. For those interested in learning more, a PDF
IML tools help expose if a model is using "protected" variables (like age or race) through proxy features, ensuring ethical and fair AI deployment. For those interested in learning more