Shapley Values for Explainable AI (The Course)
Course Info
This course includes 1 attempt.
Explain Machine Learning Models With Shapley Values
This course teaches you one of the most powerful methods to explain predictions of machine learning models. Shapley values have a solid theoretical foundation in game theory and are model-agnostic — they can be applied to any machine learning model.
What the Course Will Teach You
- Understand and interpret Shapley values correctly.
- Hands-on application of Shapley values with R and Python
- Combining Shapley explanations to get global model interpretations such as feature importance, interactions and dependence plots.
- Optional: Deep dive into the mathematical and game-theoretical foundations.
Who is this Course for?
The course has three types of goals: building intuition, hands-on code examples and theoretical foundations. It‘s an all-inclusive package which should satisfy your needs, at whatever complexity you work with Shapley values. This course is for:
- Data Scientists who want to explain their model and build it into a dashboard.
- Researcher who want to find out what the heck their machine learning model learned.
- Machine learning engineers who make explanations part of their modeling pipeline.
- Operator of machine learning models that actually have to make sense of explanations that are produced by Shapley values.
Some Course Contents
- What are Shapley values?
- How can a prediction be explained with game theory?
- What the heck are SHAP, TreeSHAP, KernelSHAP?
- How do I use Shapley values in R or Python?
- How to combine individual explanations to get an understanding of the model behavior: Shapley based feature importance, interactions and dependence plots.
- How to apply Shapley values for text and image classification tasks.
- Shapley values and feature correlation.
- Creating model explanation dashboards quickly with shapash.
Why should you trust me (Christoph Molnar)?
- I have written a book about about various way to interpret machine learning models in a free online book on Interpretable Machine Learning.
- The book already has two chapters about Shapley values and SHAP. So you can check out if you like my style of explanation and trust me expertise.
- I have studied statistics and also worked as statistician. I have also done research about interpretable machine learning and worked as data scientist. I am positioned between academia and application, which is a good place to be to translate research into practical advice.
- I am not selling Shapley values as a miracle tool. I will be very honest about the situations in which they fail and what the caveats are.
Course Material
Instructors
On a mission to make algorithms more interpretable by combining machine learning and statistics.
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