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Improve ML transparency without sacrificing accuracy


O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!



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O'Reilly Ebook:
An Introduction to Machine Learning Interpretability

Today, what makes machine learning (ML) models accurate is often what makes their predictions difficult to understand: they are very complex - this is a fundamental trade-off. Unfortunately, more accuracy almost always comes at the expense of interpretability, and interpretability is crucial for business adoption, model documentation, regulatory oversight, and human acceptance and trust.

O'Reilly begins to shed some light on this dichotomy ith An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI.

This ebook covers the following topics:

  • Social and commercial motivations for ML interpretability
  • The multiplicity of good models and model locality
  • Accurate models with approximate explanations
  • Defining interpretability
  • A ML interpretability taxonomy for applied practitioners
  • Common interpretability techniques
  • Testing interpretability
  • ML interpretability in action

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