Machine Learning Explainability
computer Online: Virtual 23. Nov 2026Details ansehen 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
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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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Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!
