Model Interpretability and Explanations
Explain what drives a model's predictions, globally and case by case, without overclaiming
A taste of a lesson
SHAP says 'years as customer' is the top feature for our retention model. Can I tell marketing that loyalty causes retention?
No, and it is an important distinction. Shapley values tell you how much the model relies on that feature to make its predictions, given the data it saw. Long tenure may simply mark customers who were always likely to stay, and it can share credit with correlated features like plan type. What you can say: 'the model leans heavily on tenure when predicting retention'. A causal claim needs an experiment or a causal design. How would you phrase this for marketing in one sentence?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose between interpretable models and post hoc explanations
- Compute and read permutation importance and partial dependence
- Interpret Shapley value attributions and their dependence on background data
- Generate feasible counterfactual explanations
- Write explanations suited to auditors, customers and engineers
Lesson plan
- 1 Who needs an explanation Match explanation types to audiences and purposes. Start
- 2 Permutation importance Measure how much a model relies on each feature. Start
- 3 Partial dependence and ICE See how predictions change as one feature varies. Start
- 4 Shapley value attributions Interpret per prediction attributions and their assumptions. Start
- 5 Local surrogates and counterfactuals Use local models and what if changes for single decisions. Start
- 6 Writing responsible explanations Present explanations accurately with caveats. Start
Try asking
About this tutor
An intermediate tutor for practitioners who need to explain models to colleagues, customers or reviewers. You will compare interpretable models with post hoc explanation methods, then work through permutation importance, partial dependence and individual conditional expectation plots, Shapley value attributions, local surrogate explanations and counterfactual explanations. Each method comes with its assumptions and failure modes, especially correlated features and the difference between explaining a model and explaining the world. You finish able to choose an explanation for an audience and write it up responsibly.
Reviews
4.3
3 ratingsSample
- Esi T.Sample
Good balance of method and caution. A worked counterfactual example with constraints would have been nice.
- Martin K.Sample
Clear on partial dependence pitfalls with correlated features. Customer facing explanation templates were useful.
- Selin A.Sample
The background dataset point explained why our SHAP values changed between reports. Very practical.
About the teacher
Experiments, causal questions and responsible models, explained for decision makers
9 tutors 437 lessons taught Sample
I help analysts and product people answer the question behind most data work: did this change cause that result? I teach A/B testing, experiment design and the basics of causal inference, plus the responsible side of modelling: fairness checks and explaining predictions. My background is in product analytics and experimentation, where I learned that a clear sentence to a decision...
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