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explainer = shap.TreeExplainer(model) shap_values = explainer.shap_values(X) The gold standard in this field
# Load the iris dataset iris = load_iris() X = iris.data y = iris.target interpretable machine learning with python pdf download
# Get the SHAP values for the test data shap_values = explainer.shap_values(X_test)
# Print the feature importance for i in range(X.shape[1]): print(f"Feature i: feature_importance[i]:.3f")
Some key concepts in interpretable machine learning include: