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Counterfactual Explainers.pdf

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Preview of Counterfactual Explainers
🔗 Source: ceur-ws.org
📊 Size: 607 KB
📄 Pages: 12 pages
⬇️ Downloads: 63

Summary

Counterfactual explanations help end-users understand how a Machine Learning model outcome could be changed to a more desirable outcome by identifying "actionable knowledge" and input dependencies that relate to outcome changes. The DisCERN algorithm uses feature relevance-based explainers like LIME or SHAP to identify the minimum subset of "actionable features" that can lead to a change in the decision. The approach is evaluated on three datasets and compared to the DiCE counterfactual method, showing promising results.

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Description

Counterfactual explanations help end-users understand how a Machine Learning model outcome could be changed to a more desirable outcome by identifying...

Technical Information

  • File Format: PDF
  • File Size: 607 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 63
  • Last Updated: 4 hours ago

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This PDF document about Counterfactual Explainers provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Counterfactual Explainers.

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