Week of 1/30

[mikeizbicki]

Dataset descriptions:

https://www.kaggle.com/datasets/camnugent/california-housing-prices

[agallagher23]

Resources to explore...

https://neptune.ai/blog/explainability-auditability-ml-definitions-techniques-tools https://docs.aws.amazon.com/whitepapers/latest/model-explainability-aws-ai-ml/interpretability-versus-explainability.html https://christophm.github.io/interpretable-ml-book/index.html

[agallagher23]

Notes for SOW

Motivating problem

• In order to make business decision, ML models
• Investigating explainable ML for reasons
  • Make business decisions better if they understand why results
  • Black box models that aren’t explainable can expose them to legal problems

Relevant definitions

• Black box model
• Explainable ML
• ...

Timeline

• Two weeks for understanding everything
• Four weeks rough draft supplemental documentation tutorial
• Month for final objective

[agallagher23]

This week:

• Finish SOW
  • Get Mike's feedback
• Research black box models generally
• Specifically dive into variable importance
• Add any resources you find really helpful to this page
• Be ready on Sunday/Monday to chat about what we've learned