Global Explainability Model Run

From The Foundation for Best Practices in Machine Learning
Technical Best Practices > Explainability > Global Explainability Model Run

Global Explainability Model Run


Document and run as many types of global explainability Models as is reasonably practical, such as Feature importances, Feature interactions, global surrogate Models, perturbation-based techniques or gradient-based techniques. When there is doubt about the stability of the techniques being used, test their quality through alternative parameterizations or by comparing across techniques.


To (a) generate global explainability of the model; (b) help promote model debugging; (c) ensure explainability fidelity and stability through numerous explainability model runs; and (d) highlight associated risks that might occur in the Product Lifecycle.

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