Machine Learning Explainability
computer Online: Virtual 23 nov. 2026Toon rooster event 23 november 2026, 09:00-17:00, Virtual, Dag 1 |
What will you learn?
After the training, you will be able to:
- Explain the use cases for model explainability
- Evaluate when model explainability is not enough (correlation vs. causality, fairness)
- Categorize the used methods into sensitivity vs. impact as well as explaining single predictions (local explainability) vs. multiple predictions (global explainability)
- Apply the explainability methods with the provided Python packages
- Summarize the advantages and disadvantages for each method
- Evaluate whether a method is appropriate for the business use case
Er zijn nog geen veelgestelde vragen over dit product. Als je een vraag hebt, neem dan contact op met onze klantenservice.
What will you learn?
After the training, you will be able to:
- Explain the use cases for model explainability
- Evaluate when model explainability is not enough (correlation vs. causality, fairness)
- Categorize the used methods into sensitivity vs. impact as well as explaining single predictions (local explainability) vs. multiple predictions (global explainability)
- Apply the explainability methods with the provided Python packages
- Summarize the advantages and disadvantages for each method
- Evaluate whether a method is appropriate for the business use case
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Er zijn nog geen veelgestelde vragen over dit product. Als je een vraag hebt, neem dan contact op met onze klantenservice.

