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Machine Learning Explainability

via Kaggle

Overview

Extract human-understandable insights from any model.

  • Why and when do you need insights?
  • What features does your model think are important?
  • How does each feature affect your predictions?
  • Understand individual predictions
  • Aggregate SHAP values for even more detailed model insights

Syllabus

  • Use Cases for Model Insights
  • Permutation Importance
  • Partial Plots
  • SHAP Values
  • Advanced Uses of SHAP Values

Taught by

Dan Becker

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